{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f7439811-4262-4955-95c4-3b927cc1c5fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://repo.huaweicloud.com/repository/pypi/simple\n",
      "Requirement already satisfied: tensorflow==2.13.0 in /opt/conda/lib/python3.11/site-packages (2.13.0)\n",
      "Requirement already satisfied: numpy==1.24.3 in /opt/conda/lib/python3.11/site-packages (1.24.3)\n",
      "Requirement already satisfied: matplotlib==3.7.2 in /opt/conda/lib/python3.11/site-packages (3.7.2)\n",
      "Requirement already satisfied: seaborn==0.12.2 in /opt/conda/lib/python3.11/site-packages (0.12.2)\n",
      "Requirement already satisfied: scikit-learn==1.3.0 in /opt/conda/lib/python3.11/site-packages (1.3.0)\n",
      "Requirement already satisfied: pandas==2.0.3 in /opt/conda/lib/python3.11/site-packages (2.0.3)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (2.3.1)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (1.6.3)\n",
      "Requirement already satisfied: flatbuffers>=23.1.21 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (25.9.23)\n",
      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (0.4.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (0.2.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (1.74.0)\n",
      "Requirement already satisfied: h5py>=2.9.0 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (3.15.1)\n",
      "Requirement already satisfied: keras<2.14,>=2.13.1 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (2.13.1)\n",
      "Requirement already satisfied: libclang>=13.0.0 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (18.1.1)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (3.4.0)\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (23.2)\n",
      "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (4.25.8)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (68.2.2)\n",
      "Requirement already satisfied: six>=1.12.0 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (1.16.0)\n",
      "Requirement already satisfied: tensorboard<2.14,>=2.13 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (2.13.0)\n",
      "Requirement already satisfied: tensorflow-estimator<2.14,>=2.13.0 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (2.13.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (3.2.0)\n",
      "Requirement already satisfied: typing-extensions<4.6.0,>=3.6.6 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (4.5.0)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (2.0.0)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /opt/conda/lib/python3.11/site-packages (from tensorflow==2.13.0) (0.37.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib==3.7.2) (1.1.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.11/site-packages (from matplotlib==3.7.2) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib==3.7.2) (4.43.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib==3.7.2) (1.4.5)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib==3.7.2) (12.0.0)\n",
      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib==3.7.2) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.11/site-packages (from matplotlib==3.7.2) (2.8.2)\n",
      "Requirement already satisfied: scipy>=1.5.0 in /opt/conda/lib/python3.11/site-packages (from scikit-learn==1.3.0) (1.15.3)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.11/site-packages (from scikit-learn==1.3.0) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.11/site-packages (from scikit-learn==1.3.0) (3.2.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.11/site-packages (from pandas==2.0.3) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.11/site-packages (from pandas==2.0.3) (2023.3)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /opt/conda/lib/python3.11/site-packages (from astunparse>=1.6.0->tensorflow==2.13.0) (0.41.2)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in /opt/conda/lib/python3.11/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.42.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /opt/conda/lib/python3.11/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (1.0.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.11/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.10)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /opt/conda/lib/python3.11/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.31.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /opt/conda/lib/python3.11/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in /opt/conda/lib/python3.11/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.1.3)\n",
      "Requirement already satisfied: cachetools<7.0,>=2.0.0 in /opt/conda/lib/python3.11/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (6.2.1)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.11/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (0.4.2)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.11/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (4.9.1)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.11/site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.0.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.3.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.11/site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2023.7.22)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in /opt/conda/lib/python3.11/site-packages (from werkzeug>=1.0.1->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.1.3)\n",
      "Requirement already satisfied: pyasn1<0.7.0,>=0.6.1 in /opt/conda/lib/python3.11/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (0.6.1)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.11/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.2.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt -i https://repo.huaweicloud.com/repository/pypi/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d145cfb5-7339-42d3-8639-8b017811b091",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-11-06 02:40:58.930983: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2025-11-06 02:40:59.466531: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2025-11-06 02:40:59.469331: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2025-11-06 02:41:01.200148: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "# 系统核心模块导入\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import seaborn as sns\n",
    "import time\n",
    "import random\n",
    "import os\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from collections import Counter\n",
    "\n",
    "# 系统配置类\n",
    "class SystemConfig:\n",
    "    \"\"\"系统配置参数\"\"\"\n",
    "    def __init__(self):\n",
    "        self.RESULTS_DIR = \"smart_wardrobe_results\"\n",
    "        self.IMAGE_SIZE = (28, 28)\n",
    "        self.BATCH_SIZE = 128\n",
    "        self.INITIAL_LR = 0.001\n",
    "        self.EPOCHS = 20\n",
    "        self.NUM_CLASSES = 10\n",
    "        \n",
    "        # 创建结果目录\n",
    "        if not os.path.exists(self.RESULTS_DIR):\n",
    "            os.makedirs(self.RESULTS_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a482298e-861c-4221-9886-43349e9e069f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据管理类\n",
    "class DataManager:\n",
    "    \"\"\"数据加载和管理\"\"\"\n",
    "    \n",
    "    def __init__(self, config):\n",
    "        self.config = config\n",
    "        self.clothing_categories = self._define_categories()\n",
    "        self.class_names = [self.clothing_categories[i]['name'] for i in range(10)]\n",
    "    \n",
    "    def _define_categories(self):\n",
    "        \"\"\"定义服装类别和业务属性\"\"\"\n",
    "        return {\n",
    "            0: {'name': 'T-Shirt/Top', 'season': 'All Seasons', 'occasion': 'Casual'},\n",
    "            1: {'name': 'Trouser', 'season': 'All Seasons', 'occasion': 'Casual/Business'},\n",
    "            2: {'name': 'Pullover', 'season': 'Fall/Winter', 'occasion': 'Casual'},\n",
    "            3: {'name': 'Dress', 'season': 'Spring/Summer', 'occasion': 'Formal/Casual'},\n",
    "            4: {'name': 'Coat', 'season': 'Fall/Winter', 'occasion': 'Casual/Business'},\n",
    "            5: {'name': 'Sandal', 'season': 'Summer', 'occasion': 'Casual'},\n",
    "            6: {'name': 'Shirt', 'season': 'All Seasons', 'occasion': 'Business/Casual'},\n",
    "            7: {'name': 'Sneaker', 'season': 'All Seasons', 'occasion': 'Sports/Casual'},\n",
    "            8: {'name': 'Bag', 'season': 'All Seasons', 'occasion': 'Universal'},\n",
    "            9: {'name': 'Ankle Boot', 'season': 'Fall/Winter', 'occasion': 'Casual/Business'}\n",
    "        }\n",
    "    \n",
    "    def load_data(self):\n",
    "        \"\"\"加载和预处理数据\"\"\"\n",
    "        print(\"📥 Loading Fashion-MNIST dataset...\")\n",
    "        (x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
    "        \n",
    "        # 数据预处理\n",
    "        x_train, x_test = self._preprocess_data(x_train, x_test)\n",
    "        \n",
    "        print(f\"✅ Dataset loaded: Train {x_train.shape}, Test {x_test.shape}\")\n",
    "        return (x_train, y_train), (x_test, y_test)\n",
    "    \n",
    "    def _preprocess_data(self, x_train, x_test):\n",
    "        \"\"\"数据预处理\"\"\"\n",
    "        # 归一化\n",
    "        x_train = x_train.astype('float32') / 255.0\n",
    "        x_test = x_test.astype('float32') / 255.0\n",
    "        \n",
    "        # 调整维度\n",
    "        x_train = x_train.reshape(-1, 28, 28, 1)\n",
    "        x_test = x_test.reshape(-1, 28, 28, 1)\n",
    "        \n",
    "        return x_train, x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c7b9a20f-070e-4ebf-9b27-077873f6825c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据分析器\n",
    "class DataAnalyzer:\n",
    "    \"\"\"数据分析和可视化\"\"\"\n",
    "    \n",
    "    def __init__(self, data_manager):\n",
    "        self.data_manager = data_manager\n",
    "    \n",
    "    def comprehensive_analysis(self, x_train, y_train):\n",
    "        \"\"\"综合数据分析\"\"\"\n",
    "        print(\"🔍 Performing comprehensive data analysis...\")\n",
    "        \n",
    "        fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "        self._plot_category_distribution(axes[0, 0], y_train)\n",
    "        self._plot_season_distribution(axes[0, 1], y_train)\n",
    "        self._plot_occasion_distribution(axes[0, 2], y_train)\n",
    "        self._plot_expected_accuracy(axes[1, 0])\n",
    "        self._plot_business_value(axes[1, 1])\n",
    "        \n",
    "        # 移除空子图\n",
    "        fig.delaxes(axes[1, 2])\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.savefig(f'{self.data_manager.config.RESULTS_DIR}/01_data_analysis.png', \n",
    "                   dpi=300, bbox_inches='tight')\n",
    "        plt.show()\n",
    "        \n",
    "        # 样本展示\n",
    "        self._display_sample_images(x_train, y_train)\n",
    "        \n",
    "        # 业务洞察\n",
    "        self._generate_business_insights(y_train)\n",
    "    \n",
    "    def _plot_category_distribution(self, ax, y_train):\n",
    "        \"\"\"绘制类别分布\"\"\"\n",
    "        category_counts = np.bincount(y_train)\n",
    "        categories = self.data_manager.class_names\n",
    "        \n",
    "        bars = ax.bar(categories, category_counts, color='skyblue', alpha=0.8)\n",
    "        ax.set_title('Clothing Category Distribution', fontsize=14, fontweight='bold')\n",
    "        ax.set_xlabel('Clothing Category')\n",
    "        ax.set_ylabel('Number of Samples')\n",
    "        ax.tick_params(axis='x', rotation=45)\n",
    "        \n",
    "        for bar, count in zip(bars, category_counts):\n",
    "            ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 50, \n",
    "                   f'{count}', ha='center', va='bottom', fontsize=10, fontweight='bold')\n",
    "    \n",
    "    def _plot_season_distribution(self, ax, y_train):\n",
    "        \"\"\"绘制季节分布\"\"\"\n",
    "        category_counts = np.bincount(y_train)\n",
    "        season_mapping = {'All Seasons': 0, 'Spring/Summer': 0, 'Fall/Winter': 0, 'Summer': 0}\n",
    "        \n",
    "        for i, count in enumerate(category_counts):\n",
    "            season = self.data_manager.clothing_categories[i]['season']\n",
    "            season_mapping[season] += count\n",
    "        \n",
    "        colors = ['lightblue', 'lightcoral', 'lightgreen', 'gold']\n",
    "        ax.pie(season_mapping.values(), labels=season_mapping.keys(), \n",
    "               autopct='%1.1f%%', colors=colors, startangle=90)\n",
    "        ax.set_title('Season Distribution', fontsize=14, fontweight='bold')\n",
    "    \n",
    "    def _plot_occasion_distribution(self, ax, y_train):\n",
    "        \"\"\"绘制场合分布\"\"\"\n",
    "        category_counts = np.bincount(y_train)\n",
    "        occasion_counts = {}\n",
    "        \n",
    "        for i, count in enumerate(category_counts):\n",
    "            occasion = self.data_manager.clothing_categories[i]['occasion']\n",
    "            occasion_counts[occasion] = occasion_counts.get(occasion, 0) + count\n",
    "        \n",
    "        ax.bar(occasion_counts.keys(), occasion_counts.values(), color='lightgreen', alpha=0.8)\n",
    "        ax.set_title('Occasion Distribution', fontsize=14, fontweight='bold')\n",
    "        ax.tick_params(axis='x', rotation=45)\n",
    "    \n",
    "    def _plot_expected_accuracy(self, ax):\n",
    "        \"\"\"绘制预期准确率\"\"\"\n",
    "        categories = self.data_manager.class_names\n",
    "        expected_accuracies = [0.92, 0.95, 0.88, 0.90, 0.87, 0.94, 0.85, 0.93, 0.96, 0.89]\n",
    "        \n",
    "        bars = ax.bar(categories, expected_accuracies, color='orange', alpha=0.7)\n",
    "        ax.set_title('Expected Accuracy by Category', fontsize=14, fontweight='bold')\n",
    "        ax.set_xlabel('Clothing Category')\n",
    "        ax.set_ylabel('Expected Accuracy')\n",
    "        ax.tick_params(axis='x', rotation=45)\n",
    "        ax.set_ylim(0, 1.0)\n",
    "        \n",
    "        for bar, acc in zip(bars, expected_accuracies):\n",
    "            ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, \n",
    "                   f'{acc:.2f}', ha='center', va='bottom', fontsize=9)\n",
    "    \n",
    "    def _plot_business_value(self, ax):\n",
    "        \"\"\"绘制商业价值\"\"\"\n",
    "        categories = self.data_manager.class_names\n",
    "        business_value = [8, 7, 6, 9, 7, 5, 8, 6, 4, 7]\n",
    "        \n",
    "        bars = ax.bar(categories, business_value, color='purple', alpha=0.7)\n",
    "        ax.set_title('Business Value by Category', fontsize=14, fontweight='bold')\n",
    "        ax.set_xlabel('Clothing Category')\n",
    "        ax.set_ylabel('Business Value Score')\n",
    "        ax.tick_params(axis='x', rotation=45)\n",
    "        \n",
    "        for bar, value in zip(bars, business_value):\n",
    "            ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1, \n",
    "                   f'{value}', ha='center', va='bottom', fontsize=10, fontweight='bold')\n",
    "    \n",
    "    def _display_sample_images(self, x_train, y_train):\n",
    "        \"\"\"显示样本图像\"\"\"\n",
    "        fig, axes = plt.subplots(3, 3, figsize=(12, 12))\n",
    "        sample_indices = random.sample(range(len(x_train)), 9)\n",
    "        \n",
    "        for i, idx in enumerate(sample_indices):\n",
    "            row = i // 3\n",
    "            col = i % 3\n",
    "            axes[row, col].imshow(x_train[idx].squeeze(), cmap='gray')\n",
    "            category_info = self.data_manager.clothing_categories[y_train[idx]]\n",
    "            axes[row, col].set_title(f\"{category_info['name']}\\nSeason: {category_info['season']}\", \n",
    "                                   fontsize=9)\n",
    "            axes[row, col].axis('off')\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.savefig(f'{self.data_manager.config.RESULTS_DIR}/02_sample_images.png', \n",
    "                   dpi=300, bbox_inches='tight')\n",
    "        plt.show()\n",
    "    \n",
    "    def _generate_business_insights(self, y_train):\n",
    "        \"\"\"生成业务洞察\"\"\"\n",
    "        category_counts = np.bincount(y_train)\n",
    "        categories = self.data_manager.class_names\n",
    "        \n",
    "        season_mapping = {'All Seasons': 0, 'Spring/Summer': 0, 'Fall/Winter': 0, 'Summer': 0}\n",
    "        for i, count in enumerate(category_counts):\n",
    "            season = self.data_manager.clothing_categories[i]['season']\n",
    "            season_mapping[season] += count\n",
    "        \n",
    "        print(\"\\n📈 Business Insights:\")\n",
    "        print(f\"• Most common category: {categories[np.argmax(category_counts)]}\")\n",
    "        print(f\"• Least common category: {categories[np.argmin(category_counts)]}\")\n",
    "        print(f\"• All-season clothing: {season_mapping['All Seasons']/len(y_train)*100:.1f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "efebaacd-fb29-447c-a9ea-216597d6648f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型构建器\n",
    "class ModelBuilder:\n",
    "    \"\"\"模型构建和训练\"\"\"\n",
    "    \n",
    "    def __init__(self, config, data_manager):\n",
    "        self.config = config\n",
    "        self.data_manager = data_manager\n",
    "    \n",
    "    def build_model(self):\n",
    "        \"\"\"构建CNN模型\"\"\"\n",
    "        print(\"🏗️ Building CNN model...\")\n",
    "        \n",
    "        model = tf.keras.Sequential([\n",
    "            # 特征提取层\n",
    "            tf.keras.layers.Conv2D(32, (3, 3), activation='relu', \n",
    "                                  input_shape=(28, 28, 1), padding='same'),\n",
    "            tf.keras.layers.BatchNormalization(),\n",
    "            tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "            tf.keras.layers.Dropout(0.25),\n",
    "            \n",
    "            tf.keras.layers.Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
    "            tf.keras.layers.BatchNormalization(),\n",
    "            tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "            tf.keras.layers.Dropout(0.25),\n",
    "            \n",
    "            tf.keras.layers.Conv2D(128, (3, 3), activation='relu', padding='same'),\n",
    "            tf.keras.layers.BatchNormalization(),\n",
    "            tf.keras.layers.Dropout(0.25),\n",
    "            \n",
    "            # 分类层\n",
    "            tf.keras.layers.Flatten(),\n",
    "            tf.keras.layers.Dense(256, activation='relu'),\n",
    "            tf.keras.layers.BatchNormalization(),\n",
    "            tf.keras.layers.Dropout(0.5),\n",
    "            tf.keras.layers.Dense(self.config.NUM_CLASSES, activation='softmax')\n",
    "        ])\n",
    "        \n",
    "        # 编译模型\n",
    "        model.compile(\n",
    "            optimizer=tf.keras.optimizers.Adam(learning_rate=self.config.INITIAL_LR),\n",
    "            loss='sparse_categorical_crossentropy',\n",
    "            metrics=['accuracy']\n",
    "        )\n",
    "        \n",
    "        print(\"✅ Model built successfully\")\n",
    "        model.summary()\n",
    "        return model\n",
    "    \n",
    "    def train_model(self, model, x_train, y_train, x_test, y_test):\n",
    "        \"\"\"训练模型\"\"\"\n",
    "        print(\"🚀 Starting model training...\")\n",
    "        \n",
    "        callbacks = [\n",
    "            tf.keras.callbacks.EarlyStopping(\n",
    "                monitor='val_accuracy', patience=5, restore_best_weights=True, verbose=1\n",
    "            ),\n",
    "            tf.keras.callbacks.ReduceLROnPlateau(\n",
    "                monitor='val_loss', factor=0.5, patience=3, min_lr=1e-7, verbose=1\n",
    "            ),\n",
    "            tf.keras.callbacks.ModelCheckpoint(\n",
    "                f'{self.config.RESULTS_DIR}/best_model.h5',\n",
    "                monitor='val_accuracy', save_best_only=True, verbose=1\n",
    "            )\n",
    "        ]\n",
    "        \n",
    "        start_time = time.time()\n",
    "        history = model.fit(\n",
    "            x_train, y_train,\n",
    "            batch_size=self.config.BATCH_SIZE,\n",
    "            epochs=self.config.EPOCHS,\n",
    "            validation_data=(x_test, y_test),\n",
    "            callbacks=callbacks,\n",
    "            verbose=1\n",
    "        )\n",
    "        \n",
    "        training_time = time.time() - start_time\n",
    "        print(f\"✅ Training completed in {training_time:.1f} seconds\")\n",
    "        \n",
    "        return history, training_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f664069-bd64-4b37-beb5-ad387b777676",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型评估器\n",
    "class ModelEvaluator:\n",
    "    \"\"\"模型评估和分析\"\"\"\n",
    "    \n",
    "    def __init__(self, data_manager):\n",
    "        self.data_manager = data_manager\n",
    "    \n",
    "    def comprehensive_evaluation(self, model, x_test, y_test, history):\n",
    "        \"\"\"综合模型评估\"\"\"\n",
    "        print(\"📊 Performing comprehensive model evaluation...\")\n",
    "        \n",
    "        # 基础评估\n",
    "        test_loss, test_accuracy = model.evaluate(x_test, y_test, verbose=0)\n",
    "        y_pred = model.predict(x_test, verbose=0)\n",
    "        y_pred_classes = np.argmax(y_pred, axis=1)\n",
    "        \n",
    "        # 创建评估仪表板\n",
    "        fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "        self._plot_confusion_matrix(axes[0, 0], y_test, y_pred_classes)\n",
    "        self._plot_learning_curve(axes[0, 1], history)\n",
    "        self._plot_loss_curve(axes[1, 0], history)\n",
    "        self._plot_class_accuracy(axes[1, 1], y_test, y_pred_classes)\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.savefig(f'{self.data_manager.config.RESULTS_DIR}/03_model_evaluation.png', \n",
    "                   dpi=300, bbox_inches='tight')\n",
    "        plt.show()\n",
    "        \n",
    "        # 详细分析\n",
    "        self._detailed_analysis(y_test, y_pred_classes, test_accuracy)\n",
    "        \n",
    "        return test_accuracy, y_pred_classes\n",
    "    \n",
    "    def _plot_confusion_matrix(self, ax, y_true, y_pred):\n",
    "        \"\"\"绘制混淆矩阵\"\"\"\n",
    "        cm = confusion_matrix(y_true, y_pred)\n",
    "        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', \n",
    "                   xticklabels=self.data_manager.class_names, \n",
    "                   yticklabels=self.data_manager.class_names,\n",
    "                   ax=ax, annot_kws={\"size\": 10})\n",
    "        ax.set_title('Confusion Matrix', fontsize=16, fontweight='bold', pad=20)\n",
    "        ax.set_xlabel('Predicted Label')\n",
    "        ax.set_ylabel('True Label')\n",
    "        ax.tick_params(axis='x', rotation=45)\n",
    "        ax.tick_params(axis='y', rotation=0)\n",
    "    \n",
    "    def _plot_learning_curve(self, ax, history):\n",
    "        \"\"\"绘制学习曲线\"\"\"\n",
    "        ax.plot(history.history['accuracy'], label='Training Accuracy', linewidth=3, color='blue')\n",
    "        ax.plot(history.history['val_accuracy'], label='Validation Accuracy', linewidth=3, color='red')\n",
    "        ax.set_title('Model Learning Curve', fontsize=16, fontweight='bold', pad=20)\n",
    "        ax.set_xlabel('Epoch')\n",
    "        ax.set_ylabel('Accuracy')\n",
    "        ax.legend(fontsize=12)\n",
    "        ax.grid(True, alpha=0.3)\n",
    "        ax.set_ylim(0.7, 1.0)\n",
    "    \n",
    "    def _plot_loss_curve(self, ax, history):\n",
    "        \"\"\"绘制损失曲线\"\"\"\n",
    "        ax.plot(history.history['loss'], label='Training Loss', linewidth=3, color='blue')\n",
    "        ax.plot(history.history['val_loss'], label='Validation Loss', linewidth=3, color='red')\n",
    "        ax.set_title('Model Loss Curve', fontsize=16, fontweight='bold', pad=20)\n",
    "        ax.set_xlabel('Epoch')\n",
    "        ax.set_ylabel('Loss')\n",
    "        ax.legend(fontsize=12)\n",
    "        ax.grid(True, alpha=0.3)\n",
    "    \n",
    "    def _plot_class_accuracy(self, ax, y_true, y_pred):\n",
    "        \"\"\"绘制各类别准确率\"\"\"\n",
    "        class_accuracies = []\n",
    "        for i in range(10):\n",
    "            mask = y_true == i\n",
    "            if np.sum(mask) > 0:\n",
    "                acc = np.mean(y_pred[mask] == y_true[mask])\n",
    "                class_accuracies.append(acc)\n",
    "        \n",
    "        # 颜色编码\n",
    "        colors = ['green' if acc > 0.9 else 'orange' if acc > 0.8 else 'red' \n",
    "                 for acc in class_accuracies]\n",
    "        \n",
    "        bars = ax.bar(self.data_manager.class_names, class_accuracies, \n",
    "                     color=colors, alpha=0.7, edgecolor='black')\n",
    "        ax.set_title('Class-wise Accuracy', fontsize=16, fontweight='bold', pad=20)\n",
    "        ax.set_xlabel('Clothing Category')\n",
    "        ax.set_ylabel('Accuracy')\n",
    "        ax.tick_params(axis='x', rotation=45)\n",
    "        ax.set_ylim(0, 1.0)\n",
    "        ax.grid(True, alpha=0.3, axis='y')\n",
    "        \n",
    "        for bar, acc in zip(bars, class_accuracies):\n",
    "            ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, \n",
    "                   f'{acc:.3f}', ha='center', va='bottom', fontsize=10, fontweight='bold')\n",
    "    \n",
    "    def _detailed_analysis(self, y_true, y_pred, test_accuracy):\n",
    "        \"\"\"详细性能分析\"\"\"\n",
    "        print(f\"\\n🎯 Model Performance:\")\n",
    "        print(f\"Test Accuracy: {test_accuracy:.4f} ({test_accuracy*100:.2f}%)\")\n",
    "        \n",
    "        print(f\"\\n📈 Class-wise Performance:\")\n",
    "        for i in range(10):\n",
    "            mask = y_true == i\n",
    "            if np.sum(mask) > 0:\n",
    "                acc = np.mean(y_pred[mask] == y_true[mask])\n",
    "                category_info = self.data_manager.clothing_categories[i]\n",
    "                \n",
    "                if acc > 0.9:\n",
    "                    status = \"✅ EXCELLENT\"\n",
    "                elif acc > 0.85:\n",
    "                    status = \"⚠️  GOOD\"\n",
    "                elif acc > 0.8:\n",
    "                    status = \"🔶 FAIR\"\n",
    "                else:\n",
    "                    status = \"❌ NEEDS IMPROVEMENT\"\n",
    "                    \n",
    "                print(f\"{status} | {category_info['name']:15} | Accuracy: {acc:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a37e77c-234b-4726-a743-1c95161ae7c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 智能衣柜系统\n",
    "class SmartWardrobeSystem:\n",
    "    \"\"\"智能衣柜应用系统\"\"\"\n",
    "    \n",
    "    def __init__(self, model, data_manager):\n",
    "        self.model = model\n",
    "        self.data_manager = data_manager\n",
    "    \n",
    "    def predict(self, image):\n",
    "        \"\"\"单张图像预测\"\"\"\n",
    "        try:\n",
    "            if len(image.shape) == 2:\n",
    "                image_input = np.expand_dims(image, -1)\n",
    "            else:\n",
    "                image_input = image.copy()\n",
    "            \n",
    "            image_input = image_input.astype('float32') / 255.0\n",
    "            image_input = np.expand_dims(image_input, 0)\n",
    "            \n",
    "            prediction = self.model.predict(image_input, verbose=0)\n",
    "            predicted_class = np.argmax(prediction[0])\n",
    "            confidence = np.max(prediction[0])\n",
    "            \n",
    "            return predicted_class, confidence\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f\"Prediction error: {e}\")\n",
    "            return 0, 0.0\n",
    "    \n",
    "    def demonstrate(self, x_test, y_test, num_samples=12):\n",
    "        \"\"\"系统演示\"\"\"\n",
    "        print(\"🏠 Smart Wardrobe System Demonstration\")\n",
    "        print(\"=\" * 50)\n",
    "        \n",
    "        # 平衡样本选择\n",
    "        demo_indices = self._select_balanced_samples(y_test, num_samples)\n",
    "        \n",
    "        fig, axes = plt.subplots(3, 4, figsize=(20, 15))\n",
    "        axes = axes.flatten()\n",
    "        \n",
    "        correct_predictions = 0\n",
    "        \n",
    "        for i, idx in enumerate(demo_indices):\n",
    "            if i >= len(axes):\n",
    "                break\n",
    "                \n",
    "            image = x_test[idx]\n",
    "            true_class = y_test[idx]\n",
    "            pred_class, confidence = self.predict(image)\n",
    "            \n",
    "            # 显示图像和结果\n",
    "            axes[i].imshow(image.squeeze(), cmap='gray')\n",
    "            \n",
    "            is_correct = pred_class == true_class\n",
    "            if is_correct:\n",
    "                color = 'green'\n",
    "                status = \"✅ CORRECT\"\n",
    "                correct_predictions += 1\n",
    "            else:\n",
    "                color = 'red'\n",
    "                status = \"❌ INCORRECT\"\n",
    "            \n",
    "            true_info = self.data_manager.clothing_categories[true_class]\n",
    "            pred_info = self.data_manager.clothing_categories[pred_class]\n",
    "            \n",
    "            title = f\"{status}\\nTrue: {true_info['name']}\\nPred: {pred_info['name']}\\nConf: {confidence:.3f}\"\n",
    "            axes[i].set_title(title, color=color, fontsize=10, pad=10)\n",
    "            axes[i].axis('off')\n",
    "            \n",
    "            # 输出结果\n",
    "            if is_correct:\n",
    "                print(f\"✅ Sample {i+1:2d}: Correctly identified as {pred_info['name']}\")\n",
    "            else:\n",
    "                print(f\"❌ Sample {i+1:2d}: Misclassified - True: {true_info['name']}, Pred: {pred_info['name']}\")\n",
    "        \n",
    "        # 清理空子图\n",
    "        for i in range(len(demo_indices), len(axes)):\n",
    "            fig.delaxes(axes[i])\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.savefig(f'{self.data_manager.config.RESULTS_DIR}/04_system_demo.png', \n",
    "                   dpi=300, bbox_inches='tight')\n",
    "        plt.show()\n",
    "        \n",
    "        # 性能统计\n",
    "        accuracy = correct_predictions / len(demo_indices)\n",
    "        print(f\"\\n📊 Demo Performance: {correct_predictions}/{len(demo_indices)} ({accuracy*100:.1f}%)\")\n",
    "    \n",
    "    def _select_balanced_samples(self, y_test, num_samples):\n",
    "        \"\"\"平衡样本选择\"\"\"\n",
    "        demo_indices = []\n",
    "        samples_per_class = max(1, num_samples // 10)\n",
    "        \n",
    "        for class_id in range(10):\n",
    "            class_indices = np.where(y_test == class_id)[0]\n",
    "            if len(class_indices) > 0:\n",
    "                selected = np.random.choice(class_indices, \n",
    "                                          size=min(samples_per_class, len(class_indices)), \n",
    "                                          replace=False)\n",
    "                demo_indices.extend(selected)\n",
    "        \n",
    "        # 补充样本\n",
    "        if len(demo_indices) < num_samples:\n",
    "            remaining = num_samples - len(demo_indices)\n",
    "            all_indices = list(range(len(y_test)))\n",
    "            available_indices = list(set(all_indices) - set(demo_indices))\n",
    "            additional_indices = np.random.choice(available_indices, remaining, replace=False)\n",
    "            demo_indices.extend(additional_indices)\n",
    "        \n",
    "        np.random.shuffle(demo_indices)\n",
    "        return demo_indices[:num_samples]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db2cdf78-8bb9-4ea6-8cfe-e2238ede683e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 错误分析器\n",
    "class ErrorAnalyzer:\n",
    "    \"\"\"错误分析和洞察\"\"\"\n",
    "    \n",
    "    def __init__(self, data_manager):\n",
    "        self.data_manager = data_manager\n",
    "    \n",
    "    def analyze_errors(self, y_true, y_pred, x_test):\n",
    "        \"\"\"错误分析\"\"\"\n",
    "        print(\"🔍 Analyzing classification errors...\")\n",
    "        \n",
    "        errors = y_true != y_pred\n",
    "        error_indices = np.where(errors)[0]\n",
    "        \n",
    "        if len(error_indices) == 0:\n",
    "            print(\"🎉 Perfect classification! No errors found.\")\n",
    "            return\n",
    "        \n",
    "        print(f\"Total errors: {len(error_indices)} ({len(error_indices)/len(y_true)*100:.2f}%)\")\n",
    "        \n",
    "        # 常见错误模式\n",
    "        self._analyze_error_patterns(y_true, y_pred, error_indices)\n",
    "        \n",
    "        # 可视化错误样本\n",
    "        self._visualize_error_samples(x_test, y_true, y_pred, error_indices)\n",
    "    \n",
    "    def _analyze_error_patterns(self, y_true, y_pred, error_indices):\n",
    "        \"\"\"分析错误模式\"\"\"\n",
    "        error_pairs = []\n",
    "        for idx in error_indices:\n",
    "            error_pairs.append((y_true[idx], y_pred[idx]))\n",
    "        \n",
    "        common_errors = Counter(error_pairs).most_common(5)\n",
    "        \n",
    "        print(f\"\\n🚫 Most Common Errors:\")\n",
    "        for (true, pred), count in common_errors:\n",
    "            true_name = self.data_manager.clothing_categories[true]['name']\n",
    "            pred_name = self.data_manager.clothing_categories[pred]['name']\n",
    "            print(f\"  {true_name:15} → {pred_name:15} : {count:3d} occurrences\")\n",
    "    \n",
    "    def _visualize_error_samples(self, x_test, y_true, y_pred, error_indices):\n",
    "        \"\"\"可视化错误样本\"\"\"\n",
    "        fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
    "        axes = axes.flatten()\n",
    "        \n",
    "        sample_errors = error_indices[:6] if len(error_indices) >= 6 else error_indices\n",
    "        \n",
    "        for i, idx in enumerate(sample_errors):\n",
    "            if i >= len(axes):\n",
    "                break\n",
    "                \n",
    "            axes[i].imshow(x_test[idx].squeeze(), cmap='gray')\n",
    "            \n",
    "            true_info = self.data_manager.clothing_categories[y_true[idx]]\n",
    "            pred_info = self.data_manager.clothing_categories[y_pred[idx]]\n",
    "            \n",
    "            title = f\"True: {true_info['name']}\\nPred: {pred_info['name']}\"\n",
    "            axes[i].set_title(title, color='red', fontsize=9, pad=10)\n",
    "            axes[i].axis('off')\n",
    "        \n",
    "        # 清理空子图\n",
    "        for i in range(len(sample_errors), len(axes)):\n",
    "            fig.delaxes(axes[i])\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.savefig(f'{self.data_manager.config.RESULTS_DIR}/05_error_analysis.png', \n",
    "                   dpi=300, bbox_inches='tight')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5788e031-37b4-4f4a-aab9-8f0c1fa37ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚀 Starting Smart Wardrobe Project Pipeline\n",
      "****************************** 1. 数据加载和预处理 ******************************\n",
      "📥 Loading Fashion-MNIST dataset...\n",
      "✅ Dataset loaded: Train (60000, 28, 28, 1), Test (10000, 28, 28, 1)\n",
      "****************************** 2. 数据分析 ******************************\n",
      "🔍 Performing comprehensive data analysis...\n"
     ]
    },
    {
     "data": {
      "image/png": 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a5Hr/G264QZ07d1a1atVUunRpGYahY8eO6bvvvlNsbKz27NmjTz75RBMmTFCrVq00bdo0fffdd+ZNg2vWrKknnnjCPF76XNdjx47VypUrJUl2u139+/dXnTp1tGfPHi1cuFCGYeidd95RixYtNGDAAEnS999/r/nz55vHqlmzpvr376+TJ09q7ty52b6GvH5u3Nwy//O4ceNGeXt7a9CgQapcubJ27NihUqVKadiwYXrzzTclSbNmzdKTTz5p7rNw4ULz5wEDBsjDwyPX73tOHn30UX388cfm8urVq/XAAw/kuM+cOXMUFxcnKe13OmjQIJUqVUqnT5/W3r17tWnTJrPtf/7zHx0/flxvvPGGue7xxx83f3fZ3Stw3bp1qlatmvr27Stvb2+dP38+T69r1apVuvHGG9WvXz+dPn1a33zzjRwOh+Lj4/Xwww9r//795rRheZV+X8EZM2bo6NGjkv43DUa6K90r5/Dhwxo8eLASExMlSWXLltWQIUNks9k0Z84chYWFKTExUUOGDFGLFi1Up06dTMc4evSoPD099cQTT8jLy0szZsxQfHy8pLTpkB955JGren0AUFI1b97c/DksLEw33nijmjZtqlatWqlFixbq3LmzateunWm/+fPnO33Pde/eXbfeeqtCQ0M1Z84cxcTEaN26dRo7dqxmzpxptqtQoYJ69+6tWrVqqUyZMnJ1ddWZM2f03Xff6eLFizpz5oxee+01ffLJJ5KkH374QRcuXJCU1gd9+OGHVbZsWZ09e1b79+/XunXrnHLlR59ly5YtuuGGGzRw4ECdOnVK8+bNkyQlJibq/fffd5qy61rccMMN6t69u37++WdJ0sGDB3X27FkFBQVlu09MTIz++9//mst9+/ZV8+bNFRkZqRMnTujPP/80t+WlX5nRoUOHJEn33HOPmjRpohMnTuT5nsJLly7V4MGDVbVqVf3www/av3+/JGnx4sX65ptv9OCDD+bpeBlNmzZNR44ccfo9PP/88+b9dnJzP+FRo0Zp7dq15nK3bt3Utm1bbdq0ScuXL5ck/fXXXxo1apRmz56d5TH++usvtWrVSt27d9fq1au1fv16SWnv36JFi9S/f/+rfo0AgKIpY7+kdOnS6tKlS5bt7r//fn377bdO+3Xs2FGSNHjwYP3999/mtvr166tnz57y8PDQjh07tHnzZnPbiRMn1K9fP/Nchc1m05133qmmTZvqwoUL+uOPP5ye183NTU2bNlXLli1Vvnx5+fn5KTY2VuvXr9fq1aslSa+++qqGDh2qypUrS5LTbW66du2qTp06KTY2VqdOndJff/1l/j1+JWvXrtW4ceNkGIYkqWrVqnrggQcUExOjL7/8UnFxcYqMjFTfvn11+PDhTPfRk6Tdu3erdOnSGjt2rOLj4/X5558rNTVVhmFo2rRp2b7feeXj46P+/fvr/fffl5TW/9q6desVb7uS2/fqas+1hoWFKSwsTF27dtWtt96qCxcuKDAwME+vbcmSJbrrrrvUpEkT/fbbb+Zn7e+//9bUqVM1adKkPB0vo6s9F5bRG2+8oQULFpjLN998s7p166Z///3XPEf477//auDAgZk+3+lye/4YyJa1NUcUdxlHFaVfsZtbynAlQ8arTpo0aWKur169uhEXF2du++STT5z2S7+i1zByvsIjqzaSjPfee8/ctmjRIqdtu3fvNrfldqRflSpVnIabN2vWzNx2zz33mOsfe+wxc72Li4vTldyXX4Wcm5F+58+fd9rn/vvvv+I+l4uIiDCWLl1qfPrpp8b06dONadOmOV1dfNtttzm1z+k9MYy0K7/c3NzMNrNnz3ba/uSTT5rbmjVrZq7v3r2705VKGa9wufz3lx+fm4xX+bi6uhrbtm3L9FpOnDjhNAIwvc2lS5ecrrLPat+s5GakX1xcnFObnj17Zrl/xvd+1KhR5vopU6ZkOualS5eMS5cumcu5ma7g8jY1atQwwsPDM7XL7Ui/cuXKOU2R+vrrrzsdf8WKFbnKltNIvStN3ZlTm9GjRzv9v7lnzx5z2549e5yu+Mt4NeLlv9OMU3e89957Ttsun5ICAJCz5ORko2XLljleTduuXTtj586dTvtl7Ic9+OCDTtsWLFhgbnNzc8s0nWZsbKyxcuVKY+bMmcY777xjTJs2zbjrrrvMfWrWrGm2zTgV+GOPPZYpf0xMjBESEmIu50efpVSpUk6jDfv06WNua968ea7e19yM9DMMw5gwYYJTuy1btmS5f/p39aVLl8x1fn5+mabLdDgcmaagv1K/8vI2l/fjM8qur3h5H/v11183t0VGRhrlypUzt9166625ypbTSL0rTd2ZU5uwsDCnvud9993ntF/Gq8tdXV2NsLAwwzAy/05at25tTmGblJRkVKhQwdyW1WwbAIDiz8vLy/wuaNq0abbtduzY4fSd8uSTTxqGkXZLkcvPVVw+XfqRI0fMny+f3WDu3LlObVNTU7P8jjxw4IAxf/5848MPPzTefvttY9q0aU7nIL/++muzbfrtWaS02ZoulzGPYWQ/4ixjX8/X19fpFkVLly51eh3po9cMw/n8gs1mc7rt0ZgxY8xtZcqUyeKdzlrG58ruvMbl/cYFCxZkuX/G/lBe36urOdc6ZsyYLNvldqTfsGHDzG1JSUlGw4YNzW033HBDrrLldE7pas6FpbdJTU01ypQpY65v27at060HLu8379ixw9yWcX1uzx8D2XERUITExcVp9+7d5nK/fv3k5eVlLl9+xe3GjRuv+rlcXV312GOPmct169Z12h4eHp7nYw4ePFi+vr7m8o033pjl8dKvZJakFi1aqGHDhubyoEGDsryiu6A4HA5NmDBBFSpUUM+ePfX444/r6aef1vjx452uLj59+nSejrt582alpKSYy4888ohsNpv5SL86XpJ27txpXvWV8b3p0aOHypcvby4//PDDWT5Xfn1uevTo4TSaIF3VqlV11113mcuff/65JGnRokVKTk6WJDVu3DjLfa+W8f9XluVF+/btzZ9feOEF3XLLLXrkkUf01ltvac2aNfLz88vySrS8GDFihAICAq56/zvvvNPpSvxBgwY5bd+2bdtVHzs/ZPxstGjRwulK/JtuuslpBG12n6OgoCCnz0t+/NsCACWZm5ub/vjjD02cODHbK4X/+usv3X777eaIu7i4OO3cudPc/vXXXzv1Q+677z5zW0pKirZs2WIuv/POOwoMDFTXrl01fPhwjRs3TuPHjzdHu0nO/aJbb71VNptNkvTZZ5+pRYsWGjx4sF577TUtW7ZMbm5uZu786rPcddddTqPtMn7X5Pf3TF77JKVLlzb7tlFRUapRo4b69Omj8ePH6+uvv9bZs2dVo0aNa8pUunRpjRgx4pqOMXjwYPNnPz8/9e7d21zevn37NR37Wm3ZskWpqanm8pAhQ5y2Z1xOTU11+vxm9Oijj8rd3V2S5O7u7vS+0x8BAFyNv/76y2n5pZdeMr9r0tWsWTPL9vXr1zdneUrn4uKi6tWrm8vHjx/Xrbfeqrp166p///566qmn9Mwzz2j8+PHmeSPJuS+W8VzITTfdpF69emnMmDH6/PPPdfjwYac8OcnY7woODlaFChXM5cvPT2XXR2vbtq2aNWtmLhemPpqUf+9VTl544YVr2j9jH83d3d2p33769GmFhoZe0/GvxYEDB3Tp0iVzedCgQU6zVV3eZ8vuc5Lb88dAdij6oUClD6WX0qb7uZovnIzCw8OdjnH5iZ1SpUrJx8fHqf3VCgwMlKenp7l8+bSMDocjz8fM2FG5/JgZjxcREWH+XLFiRad93NzcVK5cuTw9b9myZZ1eS/r0RLnxwQcfaNq0aVecUjR9usPcyvgleCWGYejixYuScn5vLl9Ol1+fm3r16mWbcdSoUebP3377reLi4pyG8+f3lI0HDx50Ws74/1p27r33Xj3zzDPy8PBQamqqNm7cqC+//FLPPfecOnfurFq1amnfvn3XlCun9yg3Mnaapcy/q4y//4wu/7clr5/H3Mr4uc3qxHLGddl9jnL6d0C6un9bAKCk8/X11RtvvKFz585p7969+uKLLzRkyBCnP5YvXLigb775RlLmvsGVpBcLFy1apKeffloxMTE5ts/Yb2rdurXeeecds6+xfft2/fe//9WLL76oHj166IYbbjCnYc+vPktu+5z54Wr6JPPmzVODBg0kSWfPntXPP/+st99+W0OGDFHVqlU1bty4a8pUq1ata75ILqc+SXx8fJZ9DSv6I5dny2q5MHxOAABFQ6VKlcyfT548mW27EydOZLnf5d9RV7qQJ2P73Fz006dPH23YsOGK7TJ+B8+YMUM333yzpLTpEZcuXar3339fw4cPV506dXT//ffn6nuvoM8HXOs508tdTR8tv96r7JQrV05ly5a96v2lqztvRB8NJQ339EOB6tKli3lPjfDwcP3888/XdF+/0qVLy2azmf9YX371RmxsrNNJmGsZtXT5lUjpV2hfi9weM+NIqcvviZaSkmLeWzC3XFxc1KlTJy1btkyStGvXLu3YscPp6qLsfPfdd+bPQUFB+umnn9S0aVPZ7XZNmDBB06ZNy1OWdJffO23s2LE53v8lffRXQECAWQC8/L0JCQnJct/8+tyUKlUq23wdO3ZUo0aNtGfPHkVGRuqzzz4z72lpt9s1cODAbPe9Gl988YXT8m233Zar/aZNm6YXXnhBGzZs0P79+3Xw4EEtXrxYZ8+e1YkTJ/Tkk0863Usnr3J6j3Lj8t/p5b+r9P83XFycr1nJOAe/w+HQkSNHrilHdjJ+brO6eizjuuw+RwXxbwsAII3NZlPDhg3VsGFDPfLII5o8ebJq1apl/nGc3i+9fFT6nXfe6XRl8+XSR+tn7Bf5+Pjoxx9/VPv27eXp6alPPvkk29FlY8aM0fDhw7Vp0ybt27dPhw4d0rJly3To0CGFhYVpyJAhOnHiRL71Wa7Xd82ZM2fM+8dJaVer59SfS9e4cWP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XF0JDQwEA169fR+7cudG9e3ed48aMGQNnZ2e9P+8Zp7TK+DwCwI8//ggnJyfMnDlTfT5fvHiBli1bYs6cOfD19cXw4cP1Wo8pXL16FTlz5tRZkywtLQ3h4eEoX768zlpFRJ8K5i/9M9f8ldmyF8D8ZUjMX8xfxsL8RURERPTp4kW/T8yCBQvUdSK++eYbuLi4IDg4GK1atYKjoyO++OIL9cRozpw5yJcvH2rWrIm5c+eq9/H2iZMhfPXVV+jSpQuePHkCV1dXBAUFqSfjy5Ytw9SpUw1ew3+1evVqtGvXDkB6Y0E7/ZHWy5cvAaRP43PlyhWj17dz505kzZoV8+fPR2Ji4nuPOX36NAYOHAgnJyeTrdGSkpKCSZMmISQkBABw7do1tUlTr149uLu7Y9GiRe8sTG8OLly4AG9vbzRr1kxt3CQlJWHjxo24fPkynj59ivLly6Nbt24AgG3btkFRFFSrVg3R0dFGr3f79u0YNWqU+nNERARq164NV1dXXL16FcDffzvFnF28eBFFixZFcnIy7ty5g/z586Nnz57q/m3btql/fvLkiV5/d8aG05w5c9CqVSu0adMGEydOVLd/9913yJkzJxo3boygoCBUqVIF5cqVA5D+Oejn52d2U6X9U9q6Hz16BB8fH0ybNu2ddX1KlSqFvn37mqI8IqNi/jI8c85fmSV7AcxfxsT8lY75S7+Yv4iIiIjIQuiTMX/+fOnZs6cUKFBAoqOjZc2aNbJ27VqZO3eurF27Vvbt2yexsbHSokULERHp1auXDB06VJKSkuTixYty584dERHJkiWLXusCICIiT548kcTERBERad68udy7d088PT2lXr16Mn/+fAEgGo1GIiIi5Pr16+qxpqatX+vq1avy4MEDuXXrltSqVUvq168vs2bNEhGRjRs3yrfffiuJiYlSvXp1KVKkiFFrTUpKktWrV0vfvn0lKChIkpOT5dy5czJ8+HAZO3asPH36VKKiomT58uWyb98+OXjwoJQuXdqoNWpZWVnJl19+KUFBQZKQkCAdO3aUWrVqyapVq2TevHny9OlTmT59uixbtswk9f2dUqVKSf/+/eXZs2cyfPhwiYmJEWtra6lfv74ULVpUwsPDxdLSUkaMGCEiIqmpqVKvXj158uSJ2NnZGbVWAHLjxg0ZO3asjB8/XkREfHx8ZMqUKVKiRAnx9/eX69evi5WVlaSlpRm1tg8RHR0tGo1GnJ2dJW/evLJ161apWrWqNGzYUH0/XrlyRX755Rc5cuSIiIg4OzvrtQYLi/R/YocOHSpjx46VIkWKSP78+WXBggXStWtXEREZM2aM/Pjjj1KkSBG5ceOGlClTRo4dOyYiIq9fv5ZSpUq98xljzjLWqiiKiIi4uLhIyZIlZcmSJXL48GGdY9zc3CRv3rxGr5PImJi/DCOz5K/MlL1EmL+MhfmL+UufmL+IiIiISIcprjSS8S1ZsgSWlpbYunUrAGDXrl3InTs3Hj16BODPEYEHDx6Ei4sLtmzZot42NDQUPj4+6Ny5M+Li4vRal/b3btmyBV988QX27NmDtLQ0XLp0CdWqVUPhwoWxa9cuAOnT2owYMQK5c+dGTEyMXuv4UEeOHFFHjx47dgy1atVC9uzZ0aVLFwB/jjgNCQlBu3btDLJuxT/Vrl07VK9eHVevXkWXLl1Qp04d+Pj44PPPP1dHyP/+++948OCBUet632ha7ejmQ4cOwcvLSx2FHRERAT8/P3To0AE3b940ap3/l4wjshcsWIAGDRogMDAQ165dU7dPmTIFhQsXVp/jYcOGYeLEiSYbzf369WvMnz8fFhYW6vRXAHDq1Ck0aNAADg4O6rof5ujt5y0qKgr58+fHrVu38OzZM9SqVQsWFhZo3769znGDBg1C5cqVDfpa//nnn1GsWDGcOHECALBu3TrY2dkhW7Zs6ro9gO6o9IcPH2LEiBHInj27OkVZZqB9D+/btw8hISHo1KkTQkNDkZaWhrS0NNSsWRMlS5bEmDFj8OuvvyIkJASOjo6IjY01ceVEhsP8ZViZJX+Za/YCmL+Yv/475i/zwPxFRERERG/jRb9PwMqVK6Eois4UHjdu3ICzszNWrlypc+ydO3fg6uqKn3/+WWf75MmTUaNGDdy/f1/v9WkXeR87dqw6lQ0AnDlzBmXLloWXlxc8PT3h7++PvHnzvrMGhqklJSUhJCQEderUAZC+JoR2nZBFixYhJSUF9+/fx7Bhw+Di4mLU6YPe18jZvHkzKlSoAEtLSwQGBmL9+vUA0qftqlChgknWkNHWeeTIEUyePBlDhw7F3r171fV29uzZAzc3N2zevBlpaWkYNWoUunXrpk7VZU4ynnj3798fXl5esLS0RKtWrdSppi5fvgwHBweULl0aNWrUgKOjIyIjI41Sn3Y6rjt37ug0bzUaDWbPnv1O4+nYsWNo0aKFSaai/SemTp2Kli1b6rxujx8/Dk9PT3UqvOjoaLi4uKBhw4ZYsWIFdu/ejT59+sDR0VHvU6glJSXp1DJ37lx89913ANKb687OzpgxYwYWLlwIS0tLdYoxrSdPnqBLly5wd3fHuXPn9FqbMYSFhcHW1hbNmzdH8+bNYWtri4YNG+LatWvQaDQIDg5G1apV4ebmhmrVqmXKx0j0TzF/GZa55q/Mkr0A5i/mr/+O+cu8MH8RERERUUa86PeRmzdvHqysrFCzZk04Ojpi1apVSEhIQEJCgro2x/bt29XjX7x4AW9vb7XplHG9jqdPn+q9vuvXr6Nw4cKYNWsWgPTRlsnJyTh58iQSExPx5MkTbNmyBd988w3WrFmj95Hu+nL69GnY2NhgzZo1AIBnz56hYcOGKFWqFJycnFCtWjW4ubkZtWGWsZEzevRoDB06FCtWrACQvq7NsWPHdI7v1asXGjdu/JdrzRja+vXrYW9vj5o1a6JSpUpQFAWDBg3C7du38eTJE1SvXh1FixZFiRIl4OzsbHbNx4x2794NRVEwffp0bN26FUOGDEGpUqXQsmVLtdETFRWFHj16YPDgwQZvRM6ZMwf79u1TR2SvW7cOrq6ucHV1RcmSJbFv3z61QaNtPI0bN069valeE//Ezp07YWNjo9OE3LFjB7y9vQH8OYL7zJkzqFOnDgoWLIjixYujdu3aem84rV+/Hs2bN0fZsmUxduxYdXtcXBweP36McuXKYdKkSQCAK1euIF++fFAUBUOGDNG5n5s3b+LWrVt6rc0Y7t69i+LFiyM0NFTdFh0djcKFC6NRo0bqtpcvX+LevXsm/cYzkaExfxmHueWvzJa9AOYvQ2L+Yv4yBuYvIiIiInobL/p9xBYuXAhFUdSF0nv06AE7OzusXr0aQPoUPQEBAahUqRKGDRuGVatWwc/PD97e3jrNprcX/tYHbVMkNjYW5cuXx5kzZ/D48WNMnTpVbZDVqFEDR48e1fvv/lAZR3CnpaWpPw8YMAB+fn7qyWJCQgJOnz6NefPmYf/+/bh9+7bRa92wYQMcHR3Rrl07dO3aFc7Ozmjbtq3OMb///jsGDBgAJycnXLhwweg1Aukn4AUKFMDChQvV53PNmjVwcXHBwIEDAaSfiM+fPx8zZ85UR2ybG41Gg7S0NHTv3h1t2rTR2bdgwQIUL14crVq1Ukdtp6amvvcbAfqsBwA8PDxQoEABHDt2DBcuXICbmxumTp2K/fv3IyAgAAUKFMC6deuQlJQEIL1ZrSgKJk+ebLDa9Gn//v2wt7dHly5dkJaWhk2bNqFMmTIAdN+vb968wYMHD/Dw4UO9f0th3rx5cHBwwP/+9z+EhITA0tISs2fPVvefPHkSBQoUUF+7V65cQbt27bBnzx6dz1tDvh4MRVvz3bt3UbhwYezduxfAn9N+/f7777CxscHChQtNViORMTF/GUZmyV+ZJXsBzF+GrAdg/tJi/jIM5i8iIiIi+iu86PeRWrduHQoUKICDBw/qbA8KCoKtra3aeDp//jxGjhwJV1dX+Pr6omnTpuqI04wnQvqmHWF48+ZNZM+eHQEBAciVKxeaNm2KiRMnYteuXShevDjmzJljsBo+xJ49e7Bx40Y8e/ZM3bZ582YUKVIEhw8fNl1hGVy7dg3u7u7qKP4rV64ge/bsCAoKUo85efIkgoOD4e3tbbTpjYD0NTMiIiJw5swZAOmjrt3d3REZGalz0r169WpYWFjg0KFDRqtNH3r37g1/f3/1vaQVEhICW1tbBAQE4NKlSwat4e1mcc2aNeHp6Ynly5dj8ODBOvtatGjxTuNp0aJFmWo9k/DwcNjb26Nfv3749ddfUaVKFezevRsHDhxAdHQ0zpw5gy1bthhkiryFCxciS5Ys2Lhxo7qtbdu2CA0NVderuXbtGgoXLoy+ffvi4sWLCAgIQLNmzdTXuyE/b/VJ+7pKTEzE8+fPdfbdu3cPjo6OWLBgAYD0x6RtPNWpU0dtIBN9zJi/DMvc85c5Zy+A+Yv5S/+Yv4yD+YuIiIiI/g1e9PtIDRgwAIUKFVJ/znjy+3bjCUhfSP7FixfqCZAhF7SPjIyEjY0Njh8/DiC98TV06FBMmzZNZ0F3f39/zJw502B1/FevX79G3759oSgKmjZtqk4XAwAdOnSAj4+PCav709mzZ1G6dGkA6c29/PnzIzg4WN0fEREBIH1qrHv37hmtrujoaPj6+qJevXpo3rw5UlNTERERgSxZsuDEiRMAoK4lAwBeXl744YcfjFafPkydOvW904mtWLECpUqVQtu2bQ36zQNtY+D69ev46aef1LWaKlasCEVREBAQ8E5DrEWLFihcuDBWrVr1zj5z9fao7L179+Kzzz6DnZ0dChcuDDc3N+TJkwceHh7Inz8/8ubNq/cp6vbv3w9FUTBmzBid7d7e3ihdujSyZcsGX19fhIaGYtq0acifPz8KFiyISpUqqc9zZhldrn1dXbx4EY0aNUK5cuXwxRdf4Pr16+oxI0eORN68eXWmLQSA2rVrv/McEX2MmL8MJzPkL3PNXgDzF/OX/jB/GRfzFxERERH9W7zo95F6+fIl/Pz8dJo2GUcyBgUFwc7ODmvWrMGrV690bmvoE6AbN26gUaNGcHBwwMmTJwHoNhlSU1MxbNgw5MqVSz1ZNkfHjh3D8OHDkStXLlSsWBGhoaEICwuDv78/tm7davR6tH9v+/fvx969e3Hx4kVUrVoVe/bsQYECBdCzZ0+1mXj+/Hl89dVXiI2NNWqNv//+O5ycnDB8+HDcvHlTZzR0YGAgSpQogWvXrqnbkpKSUL58eXXkqrnRPucxMTE4f/68zhRdFSpUQMmSJREREYGEhAQAwDfffINhw4bhyZMnBqtJ+5xeuHABxYoVQ7NmzXRGQNetWxfOzs4IDw9/Z3Rz3bp1UapUKbNf60P7vL948eKdz6+DBw/i888/R4MGDXDr1i08efIE8fHxePz48Tsjo/Xh8uXLqF69Opo0aaI2c5s3b44iRYpg7dq12LFjB0qWLAkfHx+cP38ed+/exfHjx9W/J0M2+PVJW29kZCQcHR3RrVs3zJo1C56enqhbt656XGxsLDp37oycOXNixowZ+PXXXzFo0CA4Ojoa/NsVROaA+cvwzCl/ZYbsBTB/MX/pB/OX8TF/EREREdF/wYt+H6nk5GSMGzcOHTp0UE8sNRqNzklmcHAwFEXB7t27DVpLxiaW9s83b95Eq1atkDVrVpw+fRpA+knNkiVL0LRpU+TLl++dUbqmoq35/Pnz2LhxI9atW4eHDx+q+x8+fIgePXrAz88PWbNmhaIoGDhwoNFGj2b8Pfv374ednR3CwsJw7do1+Pj4wNbWFp06ddK5zYABA1C7dm08evTIKDUCwJMnT1CtWjX069dPZ7v2ZPbIkSOoV68ePDw8EB4ejoMHD2LEiBFwcXHRaUSZm3Xr1iFnzpxwdXVF4cKFMXHiRADp0+9UrFgRbm5uqFChAr744gtYW1sjOjra4DXFxMTA2dkZQ4cOxd27d9/Z7+vri0KFCuHw4cPvTENlirUn/w3t6/23335DrVq1UK5cOdSoUQO///67OjXWvn37YGdnh+DgYLx+/drgNV2+fBn16tVDw4YN4evri3LlyumMvj5z5gwURcHmzZt1bmeI9boMQfucX7hwAXZ2dvj222/VfZs2bULDhg0RFxeHx48fQ6PR4NGjRxg1ahRy5swJLy8vVKpUCefOnTNR9UTGxfylP+acvzJL9gKYv5i/9IP5y/iYv4iIiIjov+JFv4+QdtT27du3kTt3bkybNk1nf8bG0+TJk40y0vHgwYPqtC7aE5gbN26gVatWsLOzU09IoqKi8L///c/sRiRqGwtFixZFgQIFkCNHDmzduhWJiYkA0h/T3bt3MWXKFHh7eyMqKsroNd65cwdTp07F+PHj1W3bt2+HlZUVgoKCsGvXLpw+fRohISFwcnLSGRVtDNHR0ShcuDAOHjz4lyfbp06dQvv27WFjY4MiRYqgZMmSZtN8zEj7Gn7y5Ak8PT2xdOlS7Nu3DxMnTkSWLFkwcuRI9dg5c+ZgxIgRGDx4sFHWaElMTERgYCB69+6tsz05ORlxcXFqw7RevXooUKAAjh49mmmaH1qbN29GtmzZMGLECISHh6Nq1arw9vbG9u3b1cZTeHg4FEVB7969jdIAvnz5Mvz9/eHo6Ihff/0VQHpTSaPR4MyZMyhRogSOHDli8DoM5dGjRyhWrBjKly+vs71Pnz5wdnZGgQIF4Orqiu7du+Pp06cAgMePHyM+Ph7x8fGmKJnI6Ji/9M/c85e5Zy+A+Yv5S3+Yv4yP+YuIiIiI/gte9PsIHDlyRD3RmjhxIjZs2KA2ktatW4eaNWvi/PnzOrd5e1oZQzae4uPj4e/vDxcXF3X0pfYk8PLlyyhTpgw+//xzdWoWc5tu5ezZs3B2dsbSpUvx4MEDPHjwAN27d4e9vT127NgBQHfEt3YqIWOKi4uDoihwdHTE5MmTdfatXbsW5cqVQ44cOeDl5YUKFSogMjLS6DWuXr0aVlZW6nOVsdGhfT0mJCQgJiYGjx49ws2bN40+Gv7f2Lt3L4YOHYo+ffqo77+XL19i1qxZsLS0xPDhw3WON9Y3P1NSUlC9enX89NNP6radO3ciJCQEDg4OyJ8/P1q2bAkgvfHk6OioruWTGcTFxcHHxwfTp08HkN4McXNzQ86cOZEzZ05s375dbbwfPHgQMTExRqvt6tWrCAgIQP369XHo0CF1e6NGjVCrVq1M19zL6Pbt2+jbty+8vLwwdepUAMCUKVOQLVs2LFq0CGfPnkX//v3x+eefY+7cuUhLS8vUj5fon2D+Mixzz1+ZIXsBzF/MX/rB/GUazF9ERERE9F/wol8md+3aNXh7e6NFixbo06cPLC0tdUazXrlyBc2bN8eyZcsAvNtsMpbjx4+jfv36cHNze2ch906dOsHCwgJ58uRBYmKiSU9Udu/ejfv37+ts27hxI8qVK4dnz57pNA66dOmCPHny4NmzZwD+bCoYo7mQkJCAR48eYf/+/bhz5w4A4Oeff4aiKGjVqpXO9FcA8ODBA8TExCAuLk6t19iOHj0KW1tbrF+//i+PCQ0NRd26dXXWGDJHSUlJGDFiBCwtLd8ZeattPNna2mLgwIHqdmM1neLj4+Hp6YkePXogNjYWEyZMgIeHB1q0aIGZM2di8eLFKFiwIMaNGwcA8PPzw5UrV4xSmz5cunQJkydPxqtXr3Dv3j0UKVIEvXr1ApC+jo+3tzc2bdqkNgKNTTvVVIMGDXD48GE0b94cxYoVQ3JyMoDMM6XU+9y8eRNDhw5F8eLF4e/vj88//xzh4eE6xxQqVAhBQUEmqpDIeJi/9Csz5K/MmL0A5i/mL/1g/jId5i8iIiIi+rd40S+TS05Oxpo1a5A9e3bY2dmpI0a1JzgAMGPGDLi4uKjNFEOf/GrvPzk5WWeR96ioKPj5+cHNzQ03btxQt4eEhODXX3/FH3/8YdC6/k5aWhouXbqkTkeTsXGzcOFC2NnZqSPgtQ2RK1euIH/+/Ni7d69Ra7106RI6duwIT09P2NraIlu2bGjbti3u3r2LsLAwKIqCcePG4fnz50at6/9y584d5MyZE02aNNH5+8/4ehw4cCCGDh1qtAbNv5Wxrhs3bmDMmDFQFAVz5szROe7Vq1eYOnUqcuTIgUePHhn98YSHh8PKygoFCxZEtmzZMG/ePLWxlJycjC+++AJt27Y1ak36pH0sX3/9NVq0aIGXL18CADp06ABFUVCsWDGdzx5ju3z5Mho2bIgsWbLAw8ND/Tw2t2/R/BfaxlOuXLnQtWtXdfubN2/w5s0bBAQEYNy4cdBoNGb7PibSB+Yv/cgs+SuzZi+A+cuYmL+YvwyF+YuIiIiI/g1e9MukMo5WPHr0KDw8PFCyZEm0bt1aPQHLONKybdu26Nu3r8FH8GZc5L1Zs2bw9vZG9+7dsX37dgDAxYsX4e/vD2dnZ3z77bfo0KED8uTJ887oc2PTjsBfv349smTJgn79+uHBgwcA0qevKVmyJHr06KHz/F2/fh2FCxfGgQMHjFbn+fPnkSdPHgQHB2PZsmWIiYnBkCFD4ObmBg8PD9y6dUsddT5hwgSzW8thw4YNsLGxQYcOHRAdHa1uT0hIwLBhw1CwYEGzW08I+PN1/XbT4NatWxg+fDjs7e0xb948nX0JCQnq2hqmcOvWLZw+ffqdKbrS0tIQGBiIkSNHmv0UQNrn/dq1a7h06dI702A1aNAAQ4YMUX/+3//+h3PnzqnfwDClmJgY9O3bV33NfAwNJy1t48nT01NnSrtvv/0WuXPnzlTfXCD6t5i/9Csz5K/Mnr0A5i9jYv4yLeYvIiIiIiJe9MuUMo7e69WrF1q2bImYmBisWrUKFSpUQIsWLd4ZZblo0SI0adIEt27dMnh9W7duhbW1Nfr374+xY8fCx8cHVapUQWhoKADg3r176N+/P3x8fFC3bl2cO3fO4DX9nSVLlmDVqlVITEwEkD6dlKIo6Nu3L/744w+kpaVhxowZqFKlCrp06YL4+HjcuXMH3333HQoVKmS0E9zz58/Dzs4Ow4YNe+cEdu3atShdujQqVqyIN2/eYN68eciSJQu+/fZbs2o+paWlYd68ebCysoKnpye6dOmCXr16oUmTJsiZMyfOnj1r6hLfoX2/hYeHo3PnzmjXrp1Oo+P27dsYMWIEsmXLhoULF5qqzH8kKSkJI0eORN68eXH58mVTl/O3tM97WFgYihcvDi8vL+TKlQvt2rVTa2/atCmKFy+OJUuWoFevXnB0dMTNmzdNWfZ7fUwNJy1t48nDwwOzZs3CpEmTYGtrizNnzpi6NCKDYf7Sr8yQvz6G7AUwf5ka85dpMH8RERER0aeKF/0ymYwNp3v37qFy5co4ePAggPQTm6VLl6JixYpo1aoVXr9+DSB9yp6wsDCUL19eXQDcULXFx8ejdu3aGDt2rLr94cOH6N27NypXrqwzFVN8fLza6DGV1NRUVKhQAWXKlMGGDRvUkeTaxtPXX3+NV69eITExET/99BNKlSqFLFmywMvLC/ny5TPaCdatW7fg4uKCwMBAdZtGo9E5mV2wYAE+++wzLFiwAADw/fffw9nZGY8fPzZKjf/GyZMn0bJlS5QpUwbVq1fHkCFDzLIJkrHx4eDggB49emDIkCEoVKgQmjRpon5D4fbt2/juu++gKAqWLl1qwor/2sqVK9GvXz/kypXLLJt777Nv3z7Y29tj4cKFePXqFXbs2AFFUfDzzz8DSB/NX716dZQoUQLe3t4mb2B/TP7JGlk3b95UG66WlpY4ffq0scojMjrmL/3KDPnrY8teAPOXKTB/0b/B/EVERERE+sCLfplExjU4AGDixImoV68eWrdurTOqPCkpCUuXLkWFChVQokQJ+Pv7I2fOnACAJ0+e4NChQ3qtS6PRqNPTvH79Wm3iaBep1+57/PgxSpUqhZCQEL3+/g+hPZl6/fo16tWrh/Lly2PdunV/2XjSaDR4/fo11q9fjwMHDuD27dtGq/X69euoUKECmjRpgsOHD7/3cQBAjRo10LRpU/VnU05v9H/RNmzMifb1mnHKpcjISBQrVkxdN+b69evIkycPFEVBtWrV1ObfjRs3MH78eMTGxhq/8P9DbGwsatWqhWbNmuHixYumLucfGz16NIKDgwEAV69eRZEiRRAUFPTOcffv3ze7b1VkVtrPk/j4eCQnJ+PFixc6298WFxeHMWPGmOXrnkgfmL/0L7Pkr48xewHMX8bE/EX/FPMXEREREemThZDZCw4Olr59+6o/AxAnJyc5duyYREZGiqIoIiKSmpoq1tbW0r59e5k4caIEBASIh4eH3L17V0REHB0dpXr16nqrKyUlRRRFEQsLC/nll18kODhYbt++LVmzZpW4uDj1OI1GIzly5BA/Pz+JioqStLQ0vdXwIRRFkdTUVMmaNauEhYWJk5OTTJ48WbZu3Spv3ryRpk2bSlhYmMydO1eGDBkijx49kqxZs0qLFi2kZs2akj9/fqPVWqhQIVm9erUkJyfL+PHj5ciRI+89zsLCQuzs7NSfnZycjFThv2dh8efHDwATVpJOo9GIhYWF3LhxQxYtWiQREREiInLv3j1p2LCh9OrVS27fvi1+fn7SqFEj2bt3r0RGRkpgYKCkpKRIwYIFZciQIeLh4WHiR/IuDw8PWbt2rSxdulSKFy9u6nL+EQASEREh2bNnl6SkJKlZs6bUqVNH5s2bJyIiP/30k6xZs0ZERHLnzi0ODg6mLPejAEAURZHffvtNvvrqK6lataq0b99etm7dqv478zY3NzcZPny4Wb7uiT4U85dhZJb89TFmLxHmL2Ni/qJ/gvmLiIiIiPTOdNcb6Z968eIFkpOTAUCdLujNmzdYuXIlbGxsMHDgQPXYvxq9q+81DaKiojB69GikpaXh0aNHcHd3x8yZMwEAu3btgqIo+OGHH3Ru06pVK3Tp0sVsFq7XjpzUjshOSEiAn5/fe0ecW1tbo0uXLnj48KHJ6gWAy5cvo169eggICMCRI0fU7Wlpabh9+zbq16+PZcuWAfj7aWFIl/Y1eeHCBRQrVgzNmjXDtm3b1P2RkZHQaDRo2rQp2rdvD41Gg1evXsHHxweKouCLL74wVekftRUrVqBatWpwcXFBr1691Nd0WloaunXrht69e6vvU9KPLVu2wNbWFpMnT8a6devQpUsXKIqC6OhoU5dGZHTMX4aR2fIXs5fhMH+ZJ+Yv42P+IiIiIiJ94kW/TGTZsmVwdnZWw39KSgqWLFkCKysrDB8+XD0uLS3NoE2HyMhIKIqC2bNnY9++fRg3bhyCg4N1prmaPXs2FEVB27ZtMWDAAPTs2RP29vaIiooyWF3/xcmTJ9GqVSucOHECwJ+NJx8fH6xbt05d82bt2rXInj07Hjx4YMpyAeg2nzJONzVkyBB4e3sbddrRj0lMTAycnZ0xdOhQ3L179539z58/h7e3NzZu3AggvfHbvXt3/Pbbb4iLizNytR8X7efVnTt3EBsbq/589uxZ1KhRAyVKlMCxY8cAAK9evcKIESOQN29eXLp0yWQ1f0y0TdeEhAQ0btwYU6ZMAQDcvXsXBQsWfO+UXkSfEuYv/cts+YvZy3CYv0yH+cu0mL+IiIiIyFB40S8TefLkCSpWrAhPT091XYiUlBQsXrwY1tbWGDlypMFriI6ORtasWTFq1CgAwLfffgtFUVCsWDG8fPlS59h9+/ahSZMmqFOnDpo3b44LFy4YvL5/a9WqVShTpgy++uorREREANAdcb5hwwa18fT24zOljM2ns2fPYvLkybC3t0dkZKSpS8uUEhMTERgYiN69e+tsT05Oxp07d3D58mUkJCSgfPnyaNq0Ka5fv45BgwahWLFiuH//vomq/risX78erq6ucHV1RcmSJbF//34AwG+//YaqVavC3d0d1apVQ506dZAnTx6cPXvWtAVnctOmTdNZ40uj0eD58+dwd3fHoUOH8PDhQ+TLl0+n4bR8+XI2+uiTxPylf5kxfzF76R/zl+kxfxkX8xcRERERGQMv+pmpv5oO6vnz56hSpQqKFCmi03haunQpFEXB/PnzDVZTVFQUXFxcULx4cXXbw4cPMWXKFFhYWGDu3Lnqdu00V9qpX7SNG1P6q9H3a9asQbVq1dCmTRucOnUKQHrjKSAgAIULF8bmzZv/9vamcvnyZTRq1Ag5c+ZElixZcPr0aVOXlGmlpKSgevXq+Omnn9RtO3fuREhICBwcHFCwYEF88cUXCAsLQ+HChZEvXz64urqy8fGBtCOco6Oj4e7ujqlTp2L//v0ICAhA/vz5sX79egDpnz3Lly/H119/jfnz5+Pq1aumLDvTS0xMxMSJE2Fvb49vv/1W3Z6amooOHTpg/PjxKFCgAHr27Kl+lj98+BAdO3bEypUrze6zkEifmL/072PKX8xe+sX8ZRrMX6bB/EVERERExsKLfmbm7emL1q5diwkTJmDr1q3qejLx8fFq4ykmJgZA+knztm3b9L52jFZkZCTs7OxQq1Yt5M2bF3379lX3PXv2TB1xvmLFCgDpDRrtf9qfTeF969fExMS8c9K6evVqVK9eHa1bt8a5c+cApE9j07RpU7OeOig2NhZNmjTB77//bupSMrX4+Hh4enqiR48eiI2NxYQJE+Dh4YEWLVpg5syZWLx4MYoXL46QkBD88ccfOHLkCEeY/wfa92PG9+Xx48exfPlyDB48WOfYFi1aqI2npKQko9b5KXj69ClCQ0Ph5OSEESNGqNuHDh0KRVFQv359vH79Wme7h4cHbty4YYpyiQyO+Uu/Pub8xeylP8xfxsH8ZT6Yv4iIiIjIGHjRz4x0794d3bt3V5scw4cPh729PSpWrAgLCwv06NFDHVEcHx+PqlWrwsPD451pm/TdeIqIiECWLFkwevRopKamYv78+XBxcdFpPD1//hwjR46EoihYtWqVXn//f6U9sb1z5w5++eUXrF69GuvXr4efnx969uyJa9eu6Ry/fPlyODs7o23btuoaM5lBcnKyqUv4KISHh8PKygoFCxZEtmzZMG/ePFy5cgUAkJSUhLp166Jjx44mrjLz0r4fr1+/jvnz5+PkyZMAAB8fHyiKgnr16r3zWm7RogUKFy6MVatWmcW3VT4GGS8GvHz5EtOnT4eTkxOGDRumHtOuXTvkzJkTffr0wahRo9C5c2c4OjqqDXmijw3zl359CvmL2Ut/mL8Mi/nLPDB/EREREZExWQmZjVKlSsnUqVPFwcFBGjRoIGfPnpU9e/ZI5cqVZdu2bfLNN99IUlKS9O3bV3x8fGTHjh1Svnx5GT9+vKxdu1a9Hysr/f61vn79Wnr16iWjRo0SEZHWrVuLiMiIESNERCQ0NFQcHR1l0KBBYmlpKR06dBArKyv1OFPQaDRiYWEhFy5ckGbNmomtra1cuXJFypYtK0lJSWJlZSWhoaHSv39/cXNzExGRjh07ysKFCyU8PFw+++wz8fb2FhsbG1EUxWSP45/IkiWLqUv4KNSpU0fi4uLk4cOHUrBgQXFxcVH3WVlZiaOjoxQoUEAAiIiY/evCnGjfj1FRUdKyZUspWbKk5M2bV0REIiIipEGDBnLixAk5fPiw1KxZUywtLUVEZP369fLFF1/IlClTpEmTJmJra2vKh5FpAVBfr9r/R0RESO7cuaVz586iKIqMGTNGNBqNTJo0SVavXi0jR46U2NhYOXnypJQtW1aOHj0qJUuWNOXDIDIY5i/9+VTyF7OX/jB/GQ7zl2kxfxERERGRyZj2miO9beHChXB1dUXnzp3RokULdU0WANiyZQtKlCiBjh07qiPOExIS1Dn/jUE7QjE+Pv69I86fPn2K77//Xl3vxhS0I1rPnz8POzs7fPPNN7h79y42b96M+vXro0aNGvj6669RpkwZ9O/fX50uJTExET169MD333+P27dvm6x+Mi9JSUkYOXIk8ubNi8uXL5u6nEwrJiYGzs7OGDp0KO7evfvOfl9fXxQqVAiHDx9+Z1o4vh8/zL179wCkf36npaXh2rVryJUrFyIjIwGkf27PmDEDzs7O+Oabb9TbJSUlITk52aj/xhCZCvPXh2P+In1i/tIP5i/TYf4iIiIiIlPhRT8zcPHiRURERKg/L168GI6OjihQoACio6N1jt26dStKlSqFxo0bq+vJADDJSUHGxlNISIi63RwWGb916xZcXFwQGBios33u3LlwdnbGnTt3MHv2bPj4+KB169ZYvnw5hgwZghIlSqhr9xCtXLkS/fr1Q65cuXD27FlTl5NpJSYmIjAwEL1799bZnpycjLi4ODx8+BAAUK9ePRQoUABHjx5973pQ9O+tW7cObm5uOlPmPXnyBJ6enrhz5466LWPjaeTIkaYolcjomL/0j/mL9IH5Sz+Yv0yH+YuIiIiITInTe5rYmjVr5IcffpCAgABxcHCQYsWKSdeuXcXOzk5CQkJk/vz50q9fPylcuLCIiDRq1EiSkpJk48aNUqxYMfV+tNOxGJODg4O0adNGLCwsJCgoSGxsbGTSpElmMe1OWlqauLm5SVJSkhw5ckSqVasmIqI+jy9fvpSvv/5aPvvsM1m/fr0MHz5cXFxcZOXKlZIjRw5Tlk5m4tKlS7J48WJxdnaW/fv3S/HixU1dUqZlZWUlDx48kBo1aqjbdu3aJTt37pQlS5aIg4ODVK5cWXbs2CH169eXBg0ayK5du6RSpUomrPrj4ODgIF5eXtK3b1+ZNWuWVKxYUZ4/fy5paWk603U5OztLx44dxcLCQvr37y+2trbqFIJEHyPmL8Ng/qIPxfylP8xfpsP8RURERESmpAD/f4EEMrolS5ZI//79ZeLEieLv7y+enp46c/8vWrRIRo8eLa1bt5bevXuLu7v7O/ehXavBlOLj42XTpk1SpUoVnUaYqV25ckX69esnGo1GZsyYIa6uruLu7i5dunSRyZMnq8fFx8fLq1evxNbWlg0n0vHw4UOxsbERR0dHU5eSqb148UIqVaok1atXl4EDB0pYWJgsX75cvLy8pEaNGmJvby9jx46V7t27y8iRI8Xf31/mzZsnRYoUMXXpH4WDBw/KzJkz5caNGzJ37lzJnTu3VKpUSS5cuCA5c+bUOTY+Pl7WrFkjtWvXFg8PDxNVTGRYzF+GxfxFH4r5Sz+Yv0yL+YuIiIiITIUX/Uzk5MmT0rp1a5k6daoEBgbq7Hv16pXY29uLiMiCBQtk3Lhx0qZNG+nRo4dZNXUyytgsMydXrlyR/v37y+vXr+XChQvSqVMnmT59uoiIpKamipUVv+xKZAz79u2TgIAAyZcvnzx9+lSmTp0qfn5+UqRIEUlJSZFGjRpJjhw55OeffzZ1qR+NjJ/LBw4ckJkzZ8qdO3ckODhYVq9eLQEBAeLm5iYajUZSUlIkKSlJSpYsKVWqVDFx5USGw/xlHMxfROaB+cv4mL+IiIiIyNR4xm0isbGx4urqKnXr1lW37d27V/bu3SvHjh2T3Llzy4oVKyQoKEgsLCwkODhYChYsaLZNJ3NsOImIFC1aVGbOnCnBwcHi4OAgzZo1U/eZYkouok9VnTp1JC4uTh4+fCgFCxYUFxcXdZ+lpaU4OjpK4cKFRaPRiIiY/Bs0H4OMn8u1atWS1NRUmTt3rgwdOlSePHkiDg4Osnz5clEURaytrSUtLU1+/fVXE1ZMZHjMX8bB/EVkHpi/jI/5i4iIiIhMjRf9TOTx48fy+vVrefr0qTg5OcnAgQPl1KlTkpycLKVLl5bdu3dLjRo15NSpU9K9e3fJkyeP1KtXz9RlZ0pFixaV+fPnS9++fWXChAliaWkpvr6+ZtsoI/pYubq6iqurq8625ORkGTdunBw9elS+//57Npv0QDvC/OzZs/LHH3+IRqORhg0bir+/vyiKIra2tnLhwgUZM2aMeHt7q7dLSEiQzz77zISVExke85fxMH8RmQfmL+Ng/iIiIiIic8HpPU3k4sWLUq1aNXWqlSxZssiIESOkSZMmkitXLtm2bZt07NhRdu7cKRUrVlRvl5aWxhHS/9GVK1dkwIAB8vjxY5k+fbpUrlzZ1CURfdJWrVolERERsnbtWtmxY4eULVvW1CV9NDZs2CCdO3eW3Llzy71796RFixayYsUKEREJDw+X0NBQefDggUyaNElq164tIuY7TSCRPjF/GR/zF5F5Yf4yHOYvIiIiIjIHHNJnJImJieqfNRqNlChRQg4ePCjdunWT/v37S2RkpHTr1k1y5colIunTghQsWPCdRb7ZcPrvihYtKlOnTpX8+fNL3rx5TV0O0Sft0qVLsnjxYrl9+7bs37+fDSc90I7hef36tYSGhsqcOXNk9+7dsm7dOtm+fbs6vZ6fn58MGDBA7O3tZcyYMfLmzRsRMd9pAok+BPOX6TF/EZkP5i/9Y/4iIiIiInPDb/oZwcqVK+XatWsybNgwsbGxEQCSlpYmVlbvn101MTFRWrduLdbW1rJu3TqeCOhZcnKyWFtbm7oMok/ew4cPxcbGRhwdHU1dykdjz549snLlSrG0tJTJkyerFy6OHj0qTZs2lWrVqklYWJgoiiKHDx8WNzc3yZ8/v4mrJjIM5i/zwvxFZB6Yv/SP+YuIiIiIzAm/6WdgCxYskE6dOknlypXVhpOIiJWVlZw9e1aioqLUY1++fClnzpyR5s2by82bN2XNmjWiKIq6sDrpBxtOROYhZ86cbDjp2dOnT2X9+vWyY8cO9cIGAPH19ZVNmzbJyZMnxd/fXwBI9erV2XCijxbzl/lh/iIyD8xf+sf8RURERETmhBf9DGjlypXSp08f2bZtm9SrV09tOCmKImFhYRIQECDPnj0TkfS1Ynr27Cn9+vUTa2trOXPmjGTJkkVSU1O5sDoREanevhCR8edmzZrJ6tWrJSEhQUaOHCkif04b5evrKz///LPcunVL7t69a7yCiYyM+YuIiPSN+YuIiIiIMgtO72kgy5Ytk65du4q/v7/s3r1bRNIbS5aWlrJp0yZp3ry5zJkzR4KDg9XbxMXFSUxMjNSvX18sLCwkNTX1L6egIiKiT1dsbKysXLlSgoKCpECBAjrTEKakpMjGjRulc+fO0r17dwkNDdW5bWJiomTNmtXYJRMZBfMXEREZCvMXEREREWUGvOhnAAsXLpTg4GDp2rWrbN++XVq2bCkzZ84UkfRpPtavXy/Pnj2ToKAg9TYajUZnRPnbPxMREYmkN5V8fX3l9OnTUqRIEfnyyy+lYsWKEhgYqB7z5s0b2bx5s3Tu3FmCg4Nl+vTpJqyYyDiYv4iIyFCYv4iIiIgos+AwZj2bMWOGDBgwQH777TepX7++zJ8/X53iY+bMmaIois6JgdbbDSY2nIiI6H2yZMkigYGB0rZtW/Hy8pKjR49Kz549ZcuWLVKlShUJDg4WW1tbad26tYiItG3bVqytrWXy5MkmrpzIcJi/iIjIkJi/iIiIiCiz4Df99OzgwYNy//59adOmjYiIxMfHy9q1a2XEiBHSrl07dcS5dqopIiKif+vAgQPy5ZdfSnh4uPj4+Mj9+/dlwYIFMmXKFClVqpR069ZNateuLUWKFJGNGzdK8eLFxdPT09RlExkM8xcRERka8xcRERERZQa86GcgANQ5/l+8eCG//PILG09ERKQ3gwcPlvv378uiRYvE1tZW2rRpI+fPn5dKlSrJ9evX5fjx4zJ16lTp16+fzpozRB8z5i8iIjIk5i8iIiIiMnec3tNAMgZ8BwcHdeT5yJEjxcLCQqZPn86GExER/WeVKlWSH3/8UaytraV79+5y4MABCQ8Pl5IlS8qlS5dk165d4ufnx4YTfVKYv4iIyJCYv4iIiIjI3PGbfkb04sULWbt2rfTs2VOmT58u/fv3N3VJRESUidWsWVOOHDkiuXPnlu3bt4u3t7epSyIyO8xfRESkT8xfRERERGTO+E0/I3JwcJDAwEDJmTOnNGrUyNTlEBFRJqWdwnDIkCHy4MEDmTx5snh7e+tMbUhE6Zi/iIhIH5i/iIiIiCgzsDB1AZ8aJycn+fLLL8XS0lJSU1NNXQ4REWVC2sZS+fLlRaPRyJkzZ3S2E5Eu5i8iIvpQzF9ERERElBlwek8iIqJMbNWqVRIcHCz79u2TihUrmrocIiIioo8e8xcRERERmSt+04+IiCgTq127tlSoUEHy5s1r6lKIiIiIPgnMX0RERERkrvhNPyIiokzuzZs3Ymtra+oyiIiIiD4ZzF9EREREZI540Y+IiIiIiIiIiIiIiIgok+P0nkRERERERERERERERESZHC/6EREREREREREREREREWVyvOhHRERERERERERERERElMnxoh8RERERERERERERERFRJseLfkRERERERERERERERESZHC/6EREREREREREREREREWVyvOhHRP+IoiiyadOmD7qPGzduiKIoEhkZ+ZfHLFu2TJycnD7o9xARERF9DJi/iIiIiIiI6N/gRT8ikgcPHkjfvn3F3d1dbGxsxNXVVRo3bizh4eH/+T47d+4sTZs2/de3a926tVy+fPk//95/Izk5WaZMmSLe3t5iZ2cnLi4u4uvrK0uXLpWUlJR/dB//pJFGRERE9DbmL+YvIiIiIiIifbMydQFEZFo3btwQX19fcXJykqlTp0qpUqUkJSVFdu3aJb1795bY2Fij1pM1a1bJmjWrwX9PcnKyBAQEyPnz52XcuHHi6+srDg4OcuLECfnhhx+kbNmyUqZMGYPXoW/JyclibW1t6jKIiIjobzB/MX8REREREREZAr/pR/SJ+/rrr0VRFDl16pS0aNFCihUrJiVLlpQBAwbIiRMn/vJ2UVFRUqdOHcmaNavkyJFDgoKC5NWrVyIiMnr0aFm+fLls3rxZFEURRVHkwIED6m3j4uKkdu3aYmdnJ97e3nL8+HF139vTS40ePVrKlCkjK1eulEKFComjo6O0adNGXr58qR7z8uVLad++vXz22WeSJ08emT59utSqVUtCQkL+sv4ZM2bIoUOHJDw8XHr37i1lypQRd3d3adeunZw8eVKKFi0qIiI7d+6UatWqiZOTk+TIkUMaNWok165dU+/Hzc1NRETKli0riqJIrVq11H2LFi2S4sWLi62trXh6esqcOXN0ajh27JiUKVNGbG1txcfHRzZt2vTOqPWDBw9KxYoVxcbGRvLkySNDhw6V1NRUdX+tWrWkT58+EhISIi4uLhIQECBdu3aVRo0a6fyulJQUyZkzpyxevPgvnxMiIiIyDuYv5i8iIiIiIiJD4EU/ok/Y06dPZefOndK7d2/57LPP3tn/V2u7JCQkSEBAgDg7O0tERISsW7dO9u7dK3369BERkUGDBkmrVq2kXr16cv/+fbl//75UrVpVvf2IESNk0KBBEhkZKcWKFZO2bdvqNFLedu3aNdm0aZNs27ZNtm3bJgcPHpRJkyap+wcMGCBHjx6VLVu2yJ49e+Tw4cNy9uzZv33sq1evFn9/fylbtuw7+7JkyaI+HwkJCTJgwAA5ffq0hIeHi4WFhTRr1kw0Go2IiJw6dUpERPbu3Sv379+XsLAw9f6/++47+f777yUmJkYmTJgg3377rSxfvlxERF68eCGNGzeWUqVKydmzZ2XcuHEyZMgQnTru3r0rDRo0kAoVKsj58+dl7ty5snjxYhk/frzOccuXLxdra2s5evSozJs3T7p37y47d+6U+/fvq8ds27ZNXr9+La1bt/7b54WIiIgMi/mL+YuIiIiIiMhgQESfrJMnT0JEEBYW9n8eKyLYuHEjAGDBggVwdnbGq1ev1P2//fYbLCws8ODBAwBAp06d8OWXX+rcx/Xr1yEiWLRokbotOjoaIoKYmBgAwNKlS+Ho6KjuHzVqFOzs7PDixQt12+DBg1GpUiUAwIsXL5AlSxasW7dO3f/8+XPY2dmhf//+f/l4smbNin79+v2fj/ttjx49goggKipK5zGdO3dO57jChQvj559/1tk2btw4VKlSBQAwd+5c5MiRA4mJier+hQsX6tzX8OHD4eHhAY1Gox4ze/Zs2NvbIy0tDQBQs2ZNlC1b9p06S5QogcmTJ6s/N27cGJ07d/7Xj5eIiIj0i/mL+YuIiIiIiMhQ+E0/ok8YgP90u5iYGPH29tYZne7r6ysajUYuXbr0f96+dOnS6p/z5MkjIiIPHz78y+MLFSok2bJl07mN9vi4uDhJSUmRihUrqvsdHR3Fw8Pjb2v4p4/9ypUr0rZtW3F3dxcHBwcpVKiQiIjcunXrL2+TkJAg165dk27duom9vb363/jx49WpqS5duiSlS5cWW1tb9XYZH4NI+vNcpUoVURRF3ebr6yuvXr2SO3fuqNvKly//Tg3du3eXpUuXiojIH3/8ITt27JCuXbv+o8dMREREhsP89X9j/iIiIiIiIvpvrExdABGZTtGiRUVRFImNjTXq782SJYv6Z21DRTtd0/91vPY2f3f8P1GsWLF/9LgbN24sBQsWlIULF0revHlFo9GIl5eXJCcn/+VttGvrLFy4UCpVqqSzz9LS8oPqfp/3TQ3WsWNHGTp0qBw/flyOHTsmbm5uUr16db3/biIiIvp3mL+Yv4iIiIiIiAyF3/Qj+oRlz55dAgICZPbs2ZKQkPDO/ufPn7/3dsWLF5fz58/r3Obo0aNiYWGhjvC2traWtLQ0g9Sdkbu7u2TJkkUiIiLUbfHx8XL58uW/vV27du1k7969cu7cuXf2paSkSEJCgjx58kQuXbokI0eOFD8/PylevLg8e/ZM51hra2sREZ3HmitXLsmbN6/ExcVJkSJFdP5zc3MTEREPDw+JioqSpKQk9XYZH4NI+vN8/PhxnVHxR48elWzZskn+/Pn/9vHlyJFDmjZtKkuXLpVly5ZJly5d/vZ4IiIiMg7mL+YvIiIiIiIiQ+FFP6JP3OzZsyUtLU0qVqwoGzZskCtXrkhMTIyEhoZKlSpV3nub9u3bi62trXTq1El+//132b9/v/Tt21c6dOgguXLlEpH0KaEuXLggly5dksePH0tKSopB6s+WLZt06tRJBg8eLPv375fo6Gjp1q2bWFhY6EzL9LaQkBDx9fUVPz8/mT17tpw/f17i4uLk119/lcqVK8uVK1fE2dlZcuTIIQsWLJCrV6/Kvn37ZMCAATr3kzNnTsmaNavs3LlT/vjjD4mPjxcRkTFjxsjEiRMlNDRULl++LFFRUbJ06VL58ccfRSS96aXRaCQoKEhiYmJk165d8sMPP4jIn6Pvv/76a7l9+7b07dtXYmNjZfPmzTJq1CgZMGCAWFj83x/f3bt3l+XLl0tMTIx06tTpPz2/REREpH/MX8xfREREREREhsCLfkSfOHd3dzl79qzUrl1bBg4cKF5eXlK3bl0JDw+XuXPnvvc2dnZ2smvXLnn69KlUqFBBWrZsKX5+fjJr1iz1mB49eoiHh4f4+PjI559/LkePHjXYY/jxxx+lSpUq0qhRI/H39xdfX18pXry4znotb7OxsZE9e/bIN998I/Pnz5fKlStLhQoVJDQ0VPr16ydeXl5iYWEhv/zyi5w5c0a8vLzkf//7n0ydOlXnfqysrCQ0NFTmz58vefPmlS+//FJE0hs+ixYtkqVLl0qpUqWkZs2asmzZMnWkuYODg2zdulUiIyOlTJkyMmLECPnuu+9ERNS68+XLJ9u3b5dTp06Jt7e3BAcHS7du3WTkyJH/6Hnx9/eXPHnySEBAgOTNm/dfP69ERERkGMxfzF9ERERERESGoOC/riRPRGSmEhISJF++fDJt2jTp1q2bqcv5x1avXi1dunSR+Ph4yZo16wff36tXryRfvnyydOlSad68uR4qJCIiIno/5q90zF9ERERERGRKVqYugIjoQ507d05iY2OlYsWKEh8fL2PHjhURUUd9m6sVK1aIu7u75MuXT86fPy9DhgyRVq1afXDDSaPRyOPHj2XatGni5OQkTZo00VPFREREROmYv3QxfxERERERkTngRT8i+ij88MMPcunSJbG2tpby5cvL4cOHxcXFxdRl/a0HDx7Id999Jw8ePJA8efJIYGCgfP/99x98v7du3RI3NzfJnz+/LFu2TKys+FFPRERE+sf89SfmLyIiIiIiMgec3pOIiIiIiIiIiIiIiIgok7MwdQFERERERERERERERERE9GF40Y+IiIiIiIiIiIiIiIgok+NFPyIiIiIiIiIiIiIiIqJMjhf9iIiIiIiIiIiIiIiIiDI5XvQjIiIiIiIiIiIiIiIiyuR40Y+IiIiIiIiIiIiIiIgok+NFPyIiIiIiIiIiIiIiIqJMjhf9iIiIiIiIiIiIiIiIiDK5/wfv0xULN1k8dwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1800x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📈 Business Insights:\n",
      "• Most common category: T-Shirt/Top\n",
      "• Least common category: T-Shirt/Top\n",
      "• All-season clothing: 50.0%\n",
      "****************************** 3. 模型构建和训练 ******************************\n",
      "🏗️ Building CNN model...\n",
      "✅ Model built successfully\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 32)        320       \n",
      "                                                                 \n",
      " batch_normalization (Batch  (None, 28, 28, 32)        128       \n",
      " Normalization)                                                  \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 14, 14, 32)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 14, 14, 32)        0         \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 14, 14, 64)        18496     \n",
      "                                                                 \n",
      " batch_normalization_1 (Bat  (None, 14, 14, 64)        256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 7, 7, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 7, 7, 64)          0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 7, 7, 128)         73856     \n",
      "                                                                 \n",
      " batch_normalization_2 (Bat  (None, 7, 7, 128)         512       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 7, 7, 128)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 6272)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               1605888   \n",
      "                                                                 \n",
      " batch_normalization_3 (Bat  (None, 256)               1024      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                2570      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1703050 (6.50 MB)\n",
      "Trainable params: 1702090 (6.49 MB)\n",
      "Non-trainable params: 960 (3.75 KB)\n",
      "_________________________________________________________________\n",
      "🚀 Starting model training...\n",
      "Epoch 1/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.4933 - accuracy: 0.8271\n",
      "Epoch 1: val_accuracy improved from -inf to 0.78680, saving model to smart_wardrobe_results/best_model.h5\n",
      "469/469 [==============================] - 38s 79ms/step - loss: 0.4933 - accuracy: 0.8271 - val_loss: 0.5771 - val_accuracy: 0.7868 - lr: 0.0010\n",
      "Epoch 2/20\n",
      "  1/469 [..............................] - ETA: 32s - loss: 0.3852 - accuracy: 0.8438"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.11/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "469/469 [==============================] - ETA: 0s - loss: 0.3232 - accuracy: 0.8824\n",
      "Epoch 2: val_accuracy improved from 0.78680 to 0.88860, saving model to smart_wardrobe_results/best_model.h5\n",
      "469/469 [==============================] - 36s 77ms/step - loss: 0.3232 - accuracy: 0.8824 - val_loss: 0.3059 - val_accuracy: 0.8886 - lr: 0.0010\n",
      "Epoch 3/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.2818 - accuracy: 0.8966\n",
      "Epoch 3: val_accuracy did not improve from 0.88860\n",
      "469/469 [==============================] - 36s 77ms/step - loss: 0.2818 - accuracy: 0.8966 - val_loss: 0.3062 - val_accuracy: 0.8830 - lr: 0.0010\n",
      "Epoch 4/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.2578 - accuracy: 0.9059\n",
      "Epoch 4: val_accuracy improved from 0.88860 to 0.90300, saving model to smart_wardrobe_results/best_model.h5\n",
      "469/469 [==============================] - 36s 77ms/step - loss: 0.2578 - accuracy: 0.9059 - val_loss: 0.2621 - val_accuracy: 0.9030 - lr: 0.0010\n",
      "Epoch 5/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.2425 - accuracy: 0.9109\n",
      "Epoch 5: val_accuracy improved from 0.90300 to 0.91270, saving model to smart_wardrobe_results/best_model.h5\n",
      "469/469 [==============================] - 36s 76ms/step - loss: 0.2425 - accuracy: 0.9109 - val_loss: 0.2465 - val_accuracy: 0.9127 - lr: 0.0010\n",
      "Epoch 6/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.2299 - accuracy: 0.9157\n",
      "Epoch 6: val_accuracy did not improve from 0.91270\n",
      "469/469 [==============================] - 36s 76ms/step - loss: 0.2299 - accuracy: 0.9157 - val_loss: 0.2340 - val_accuracy: 0.9118 - lr: 0.0010\n",
      "Epoch 7/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.2193 - accuracy: 0.9193\n",
      "Epoch 7: val_accuracy did not improve from 0.91270\n",
      "469/469 [==============================] - 35s 75ms/step - loss: 0.2193 - accuracy: 0.9193 - val_loss: 0.2532 - val_accuracy: 0.9112 - lr: 0.0010\n",
      "Epoch 8/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.2066 - accuracy: 0.9230\n",
      "Epoch 8: val_accuracy improved from 0.91270 to 0.92390, saving model to smart_wardrobe_results/best_model.h5\n",
      "469/469 [==============================] - 35s 75ms/step - loss: 0.2066 - accuracy: 0.9230 - val_loss: 0.2153 - val_accuracy: 0.9239 - lr: 0.0010\n",
      "Epoch 9/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.2007 - accuracy: 0.9262\n",
      "Epoch 9: val_accuracy did not improve from 0.92390\n",
      "469/469 [==============================] - 35s 74ms/step - loss: 0.2007 - accuracy: 0.9262 - val_loss: 0.3068 - val_accuracy: 0.8970 - lr: 0.0010\n",
      "Epoch 10/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.1936 - accuracy: 0.9286\n",
      "Epoch 10: val_accuracy did not improve from 0.92390\n",
      "469/469 [==============================] - 35s 74ms/step - loss: 0.1936 - accuracy: 0.9286 - val_loss: 0.2099 - val_accuracy: 0.9236 - lr: 0.0010\n",
      "Epoch 11/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.1837 - accuracy: 0.9313\n",
      "Epoch 11: val_accuracy did not improve from 0.92390\n",
      "469/469 [==============================] - 35s 75ms/step - loss: 0.1837 - accuracy: 0.9313 - val_loss: 0.2368 - val_accuracy: 0.9181 - lr: 0.0010\n",
      "Epoch 12/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.1811 - accuracy: 0.9333\n",
      "Epoch 12: val_accuracy did not improve from 0.92390\n",
      "469/469 [==============================] - 35s 74ms/step - loss: 0.1811 - accuracy: 0.9333 - val_loss: 0.2791 - val_accuracy: 0.9003 - lr: 0.0010\n",
      "Epoch 13/20\n",
      "469/469 [==============================] - ETA: 0s - loss: 0.1767 - accuracy: 0.9346Restoring model weights from the end of the best epoch: 8.\n",
      "\n",
      "Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "\n",
      "Epoch 13: val_accuracy did not improve from 0.92390\n",
      "469/469 [==============================] - 35s 75ms/step - loss: 0.1767 - accuracy: 0.9346 - val_loss: 0.2988 - val_accuracy: 0.8965 - lr: 0.0010\n",
      "Epoch 13: early stopping\n",
      "✅ Training completed in 462.8 seconds\n",
      "****************************** 4. 模型评估 ******************************\n",
      "📊 Performing comprehensive model evaluation...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🎯 Model Performance:\n",
      "Test Accuracy: 0.9239 (92.39%)\n",
      "\n",
      "📈 Class-wise Performance:\n",
      "✅ EXCELLENT | T-Shirt/Top     | Accuracy: 0.920\n",
      "✅ EXCELLENT | Trouser         | Accuracy: 0.978\n",
      "⚠️  GOOD | Pullover        | Accuracy: 0.890\n",
      "✅ EXCELLENT | Dress           | Accuracy: 0.925\n",
      "⚠️  GOOD | Coat            | Accuracy: 0.900\n",
      "✅ EXCELLENT | Sandal          | Accuracy: 0.981\n",
      "❌ NEEDS IMPROVEMENT | Shirt           | Accuracy: 0.716\n",
      "✅ EXCELLENT | Sneaker         | Accuracy: 0.980\n",
      "✅ EXCELLENT | Bag             | Accuracy: 0.987\n",
      "✅ EXCELLENT | Ankle Boot      | Accuracy: 0.962\n",
      "****************************** 5. 系统演示 ******************************\n",
      "🏠 Smart Wardrobe System Demonstration\n",
      "==================================================\n",
      "❌ Sample  1: Misclassified - True: T-Shirt/Top, Pred: Sneaker\n",
      "❌ Sample  2: Misclassified - True: Shirt, Pred: Sneaker\n",
      "❌ Sample  3: Misclassified - True: Pullover, Pred: Sneaker\n",
      "❌ Sample  4: Misclassified - True: Trouser, Pred: Sneaker\n",
      "❌ Sample  5: Misclassified - True: Coat, Pred: Sneaker\n",
      "❌ Sample  6: Misclassified - True: Bag, Pred: Sneaker\n",
      "❌ Sample  7: Misclassified - True: Sandal, Pred: Sneaker\n",
      "✅ Sample  8: Correctly identified as Sneaker\n",
      "❌ Sample  9: Misclassified - True: Coat, Pred: Sneaker\n",
      "❌ Sample 10: Misclassified - True: Bag, Pred: Sneaker\n",
      "❌ Sample 11: Misclassified - True: Dress, Pred: Sneaker\n",
      "❌ Sample 12: Misclassified - True: Ankle Boot, Pred: Sneaker\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_37157/2759040002.py:80: UserWarning: Glyph 10060 (\\N{CROSS MARK}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_37157/2759040002.py:80: UserWarning: Glyph 9989 (\\N{WHITE HEAVY CHECK MARK}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_37157/2759040002.py:81: UserWarning: Glyph 10060 (\\N{CROSS MARK}) missing from current font.\n",
      "  plt.savefig(f'{self.data_manager.config.RESULTS_DIR}/04_system_demo.png',\n",
      "/tmp/ipykernel_37157/2759040002.py:81: UserWarning: Glyph 9989 (\\N{WHITE HEAVY CHECK MARK}) missing from current font.\n",
      "  plt.savefig(f'{self.data_manager.config.RESULTS_DIR}/04_system_demo.png',\n",
      "/opt/conda/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 10060 (\\N{CROSS MARK}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/conda/lib/python3.11/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 9989 (\\N{WHITE HEAVY CHECK MARK}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Demo Performance: 1/12 (8.3%)\n",
      "****************************** 6. 错误分析 ******************************\n",
      "🔍 Analyzing classification errors...\n",
      "Total errors: 761 (7.61%)\n",
      "\n",
      "🚫 Most Common Errors:\n",
      "  Shirt           → T-Shirt/Top     : 125 occurrences\n",
      "  Shirt           → Coat            :  78 occurrences\n",
      "  T-Shirt/Top     → Shirt           :  58 occurrences\n",
      "  Shirt           → Pullover        :  57 occurrences\n",
      "  Coat            → Shirt           :  46 occurrences\n"
     ]
    },
    {
     "data": {
      "image/png": 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fSemoo/JnZ73//XkBvu++/BajzZtTes978mlPelJKL33pA8f6xCce/oscvvrVlM4+O7+Ce8IJefHfu5h+7nPzIl+p5KOuzjvvga/+ppTSW9+a0qxZ+TK98IUTnzX2ne/ko6n/8A/zTu/ZZ+eFbm+veU3eAW5uzq/kjo/nxRwAJovJtK6nlN/tdNJJeW1+9asf/jNCe3ryzn9LS0pTphzgFQMAB1FJ6+2B+Md/zKX0Zz+bj46ePTvPbW/Pe14+qrmxMe8jr16di+KHMj6e0t/9XV6XH25tvvzyvE3wcD7/+bxd8PSn533yN70ppalT84vVe7zsZfk6amnJ19nGjfkocniUUVzDHhdfnF9J3bAhpf/8z5Qe97iJ0xYunPj/zZvzW4ae/vS8QPf25rfqtrTkBXfdulwMNzdPZBYtOrC5dHfnxeeGG/KRV3/yJym96lUTb/+54or89qMZM/LO66c+lee1tzlzJv6/o2PiiOl1635zPnv/PD6ed4aXLcvz6O3Nr+w++O8DQMkm07qe0m+u2/39+/578+blEgAA6q2k9XZfLrroN7/0sLU1pTe/OaWf/Szv737kIym9/e35Yzz2ePDanNK+34nc1bV/X5R8+eX5yPKHs2ZN/piwvR19dP79HntfN83N+cWAtWsfeXyYZGzxwv7Ye+dw+vT8Cup11+UFes+/3btzmTx3bl509/5ChnvvrX3szs68oPb0pHTLLSkND+fPpf7DP8wL044defF9qLc2PZS5c/MrxXvbe35f/GL+993v5r+9fXsee3//PgCUrqR1PTr/h/oZAEpQz/V2b5/61MN/6WFraz7C+cQTH/gdEwdif9bmm2/O+9WPfezD/6358/MXR+5t1ar8+z323qcfGckfvTJv3oHMGCYFW7lwoBoaclH85jfnV4ZTym8XuvTS/P9PfGJK06al9Nd/nUvm666bOG1/veUtKa1YkfPDw/mzvQYG8hclDg2lNDiYF/7W1vz3H/x244fzu7+bC+9/+Zf8GVjf/W7+LOw9+vryq94zZuSx3/c+n28NwKNXvdf1g2H27LxW33//wfl7AHCwHY719kC89735aOvdu1MaG8sfcXLLLfnjNw6GmTPzZb7rronf7c/HhKSUS/QvfCF/jMroaEr/9E/5unre8ybOc+ml+Tras88+c2a+DuFRRnENtbj44rygPeMZ+S1Bp5468Zai5ua86F1xRV543/72lF772gfmL7roN7+xeG9DQ/kzq6ZPz29P+sxnUvrWt/Lbhbq6Uvr4x1N63evyW4//5m9+87O/Hs60aflvfexj+a1M//qvE18UkVL+XM09n7+5ZEn+LLK9X9kFgEebeq7rB8Py5flLr44/Pq/tP/nJwfm7AHAwHer19kA0NeUvQJw9O6/P73lP3jd+8pMPzt9vb0/p3e/O30/V25sPNtufjwlJKaXf/u1cVl94YZ7bl7+cv4h5748iee1rU3rb2/J19f3vp3TZZfkywaNMpVr1/n8AAAAAOCQ2bUrpuOPyZ4Hv/TnetTjjjJTOPTd/aSM8yjniGgAAAAAOlW3bUvroR+OlNRxhvI8AAAAAAA6VY4/N/4AD4qNCAAAAAAAoio8KAQAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIrStL9nrFQqh3IeAFCMarVa7ykcFNZu6uXcc88N5a+++upQfsuWLaE8tWtrawvlBwcHD9JMJqfI8/ajZe2q1aPl8lu7j1wNDbHjCsfHxw/STOrjzW9+cyh/9tln15zdvXt3aOzm5uZQ/pprrgnl3/Wud4XyUdH7btRkv+8fyfZn7XbENQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFqVSr1ep+nbFSOdRzAYAi7OfSWDxr9+QVve2OP/74uo5/1llnhfIf/OAHQ/nvfOc7ofwvfvGLUD6ioSF2XMmpp54ayj/rWc8K5S+++OJQ/sorrwzl169fH8qvXr06lKd+rN2PDpHLH70PNDU1hfKjo6Oh/GTX2NgYyo+MjITykdt/fHw8NHb0skfNnTs3lN+wYcNBmsnk1NLSEspHHvvR+95ktz+PW0dcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFCUSrVare7XGSuVQz0XACjCfi6NxbN2109PT08of/rpp4fyu3btCuXHx8dD+euvvz6UP++880L5t7zlLaF85Pa75ZZbQmMvXrw4lI/edv/2b/8Wyn/sYx8L5U899dRQfurUqaH8unXrQvkbb7wxlKd21u4yROcfyUef/ya75zznOaH8+eefH8qfddZZofz69etD+Y6OjpqzM2fODI09Ojoaym/cuDGUX7p0aSh/3XXXhfJf/vKXQ/mvf/3rofyGDRtCeepnf9ZuR1wDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUJRKtVqt7tcZK5VDPRcAKMJ+Lo3Fs3bXz1Of+tRQftu2baH84OBgKN/W1hbKd3R0hPJ33HFHKL99+/ZQ/oQTTqg5+7u/+7uhsa+66qpQfsWKFaF8Q0PsuJZjjz02lI/ed0dHR0P5xYsXh/LXXnttKN/f3x/KH8ms3URF1+43vvGNofxxxx0Xyk+fPj2UHx8fD+Wjz1+dnZ2h/MDAQM3ZqVOnhsZubW0N5aPWrl0byke325qbm0P5kZGRUD56+T/0oQ+F8t/61rdqzka3u6KP23rbn7XbEdcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFKVSrVar+3XGSuVQzwUAirCfS2PxJvvaHZ1/5Hbs6ekJjf30pz89lL/nnntC+eh9OHrdNzU1hfJTpkwJ5UdHR0P59evX15zt7u4Ojd3e3h7KR6/7aL6/vz+Uj9520Xz09tu4cWMov3r16lD+SGbtLkNLS0soPzw8XHP2uc99bmjsf/u3fwvld+3aFcoPDAyE8iMjI6F89DHU2toayo+NjdVt/M2bN4fGnjZtWig/NDQUykdF187otkNDQ+yY2ui2U1dXVyh/zjnn1Jz95S9/GRq7nvtLB8P+jO+IawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKE31ngAAwEOpVqt1G/uoo44K5YeGhkL56GVvbm4O5RsbG0P5sbGxUH54eDiUb2iIHZvR2tpac3ZwcDA09tSpU0P53bt3h/LR+Y+Ojoby0ft+9LaP3vdmz54dyq9evTqUh3qLPoYizj///FB+165dofy2bdtC+UqlEsq3tLSE8tHbLrp+RJ//I9su0duuo6MjlK/nNm9KKbW1tYXy0flH8zt27Ajlo9vN73znO2vOvuAFLwiNXe/7zuHgiGsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAitJU7wkAAJRmxowZoXxDQ+zYgKam+m6ijY2NhfLVavUgzaQ2fX19ofycOXNqzi5atCg09nXXXRfKR++7o6OjoXxjY2MoH73vVSqVUL6lpaWu+cj86/24g3pbtmxZKN/f3x/Kt7e3h/I7d+4M5cfHx0P56LZL9Pm3npYvXx7KDwwMhPLR6z4qet+JbjtE16/o2hu9/U466aSas62traGxh4aGQvnJwBHXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABSlqd4T4NGvsbExlB8bGztIMznyfOUrXwnlW1tb6zr+jTfeGMqvWrUqlN+9e3coX+/77oIFC2rOnnzyyaGxjz322FD+wx/+cCgPKcWewzo7O0Njd3d3h/I7d+4M5UdGRkL54eHhuo4/NDQUyjc1xTZxN2/eXHN2fHw8NPbs2bND+U2bNoXyUc3NzaF8dO2M3vYdHR2hfPS+G7n9N2zYEBobSrB8+fKaszNmzAiN3dfXF8pXKpVQPvr8GV1/ovvt0fGj+YaG2o/LHBwcDI1d784jet+r53WfUkrVajWUj67do6OjofyUKVNqzp5//vmhsS+55JJQfjJwxDUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARWmq9wR49BsfH6/r+I2NjaH82NjYQZrJgbvvvvtC+a1bt4by99xzTyj/5je/OZSvVCp1zQ8ODoby7e3tofzw8HAo39raWnN2ZGQkNPbChQtD+V/+8peh/FVXXRXK8+jQ2dlZc7anpyc09owZM0L5TZs2hfLR56+WlpZQPrr2Ruff1tYWym/cuLHmbEdHR2jsyHN3SvG1J7p2RlWr1VB+ypQpoXz0vhsVve/CZPfiF7+45mz08Rt9/omOX+/n36GhoVC+oaG+x0VGtl2i+x4nnnhiKF/v277e40c7o+jaH+18IvvtZ511VmjsSy65JJSfDBxxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRmuo9AR79qtVqKN/Y2BjKj42NhfJRX/va12rOfvOb3wyNvWvXrlD+ec97Xijf1dUVyg8ODobyzc3NoXzUrFmzQvnR0dFQPvLY2717d2js6HXf09MTykNKseeglpaW0Njt7e2h/MDAQF3Hjz4HRMePrt0jIyOhfOT5u62tLTT29u3bQ/no+NHtrui2R3d3dyi/dOnSUL6vry+Uj267TJkyJZSHye6cc86pORt9/oyu/XPmzAnl169fH8rv3LkzlB8fHw/lo+tHdPyIU045JZSPbndEr7vofmN0/pVKJZSPXv7otk/0+tuyZUvN2d/+7d8OjX0kcMQ1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVpqvcE4JGMjY3VdfxLLrkklP/Qhz5Uc/baa68NjR31j//4j6H8iSeeGMo/9rGPrev4vb29ofytt94ayg8ODobyO3furDl75ZVXhsY+44wzQvnobfftb387lOfRYfr06TVn+/v7Q2NH78PTpk0L5aPzb2xsrGs+Knr5zzrrrJqz0bXjy1/+cijf0dERyre3t4fy999/fyg/c+bMUH758uWh/MqVK0P5bdu2hfLR6x8mu7a2tpqzQ0NDobG7urpC+YaG2HGB3d3doXxUvbcdourZG1QqlbqNnVL9r/vofTe69kUfe9VqNZQfGBioOTs6Ohoa+0jgiGsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAitJU7wlQvkqlEspXq9WDNJPafOQjHwnln/e854XyF154YShfT+vWratr/oorrgjlqZ9FixaF8q94xStC+b/5m78J5Xl0aG9vrzm7ffv20NgLFy4M5YeGhkL56Py7u7tD+YaG2LER0W2HyG2fUkorV66sOTtt2rTQ2M3NzaF8S0tLKF/v2y56+adPnx7K9/b2hvKrVq0K5aP3Xai3F7/4xaF8V1dXzdmRkZHQ2KOjo6F89Pmzra0tlN+8eXMoH33+jRofHw/lI9f/8PBwaOzodRftXKKi993o5Y/e9tG1c+fOnaF85PqbO3duaOznP//5ofy3vvWtUP5wcMQ1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVpqvcE9ldjY2Nd85VKpebs+Ph4aOyRkZFQPqpardZ1/Pe+972h/PLly0P5/v7+UL6e6nm/PxjGxsZC+Xrfd6Oe+cxnhvLnn39+zdmXvvSlobG3b98eym/bti2UnzZtWihPGVpbW0P55ubmmrNr164Njd3b2xvKz5kzJ5S/8847Q/mZM2eG8pHrPqX4839TU2wTd/fu3TVnTzrppNDY//M//xPK11t7e3sov2rVqlA++rwxd+7cUP6KK64I5ZcsWRLKQ70dc8wxofymTZtqzkaff2bMmBHKt7S0hPJ33313KB+9/NH93tHR0VA+2ptEtj2ia8/SpUtD+eh+e3S7Kdo5tbW1hfLRtfOVr3xlKN/T0xPKr169uubsL3/5y9DYK1asCOUnA0dcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFCUpnpPYH+NjY3VNX8kW7JkSSh/8cUXh/JHHXVUKL927dpQfs6cOaH8rl27as5GL/uOHTtC+SPdaaedFsp/4xvfCOVHRkZC+R/84Ac1Z6+88srQ2EuXLg3lq9VqKH/ccceF8pShra0tlG9ubq4529fXFxq7tbU1lH/84x8fyn/3u98N5SPXXUopdXR0hPLj4+OhfFTkOWzu3LmhsQcHB0P5KVOmhPLRtSdqy5YtoXz0vjt16tRQfvfu3aF85ParVCqhsaNrL6QU3/eL5Ht7e0Njz58/P5S/8847Q/kLL7wwlP+nf/qnUP66664L5aPPv01NsXoqsn5Gt7ui241R0ef/6LbH9OnTQ/n77rsvlI9e/mOPPTaUv/3220N5Hp4jrgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAojTVewL7a+rUqaH8CSecEMo3NzfXnN22bVto7Kc//emh/LJly0L5448/PpRft25dKH/99deH8osWLQrlV6xYEcovX7685uz27dtDY3/6058O5S+66KJQvt6ij/srr7wylB8aGgrl+/v7Q/kXvvCFdRu7r68vlJ85c2YoP3v27FCeMjQ1xTZTGhsba852d3eHxo4+hqL34YGBgVB+fHw8lO/p6anr+JHbPqWUNm3aVHM2su4fDB0dHaF89L4TfdxGHzstLS2hfG9vbyg/ODgYykeuv/b29tDYu3btCuWh3qL7TtF81IwZM0L5jRs3hvKjo6OhfHTtj64/DQ21H5cZveyVSiWUj273VKvVUL61tTWUX79+fSgf3feLuv322+s6fkR0m3dsbOwgzeTQccQ1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVpOlwDveIVrwjl3/KWt4TyP/3pT0P5/v7+mrPHHHNMaOzm5uZQ/u677w7lP/GJT4TyN998cyj/nOc8J5SPXv/Dw8Oh/B133FFzduPGjaGxX/CCF4Tyr3nNa0L597///aH8Bz7wgVD+e9/7XihfrVZD+e3bt4fyU6ZMCeU3b95cc3ZoaCg09uDgYCg/MjISyl9zzTWhPGXo7OwM5fv6+mrOLl26NDT2vffeG8o/9alPDeUbGmLHJoyNjYXyTU2xTczx8fFQvrW1NZTftm1bzdn169eHxo7edl1dXaF8dO1qbGwM5aO3/datW0P5mTNnhvKjo6OhfHd3d83Z6H0HSlCpVGrORh8D0Xx0+7W3t7eu43d0dITy0W2H6L5Xe3t7zdnVq1eHxp4+fXooH73uomtnW1tbKB+97aL3vah6rp/R2y5635kMbN0AAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUJSmwzXQ2WefHcqPjY2F8kuWLAnl77vvvpqz9957b2jsn/3sZ6H8jBkzQvljjz22rvnTTz89lJ8+fXoo39nZGcr39vbWnK1UKqGxt23bFsqvWbMmlD/nnHNC+Re+8IWh/NDQUCi/ZcuWUD5qcHAwlB8fH68529LSEhq7ubk5lJ8yZUooP2fOnFCeMrS2tobymzZtqjn71Kc+NTR2R0dHKP/1r389lG9sbAzlBwYGQvmo6HNA1O7du2vOfu1rXwuN3d7eHsp3dXWF8n19faF8vZ//b7vttlD+6KOPDuWj+yyR7caRkZHQ2FCCarVaczb6+ItsOx8M0W3/6L5jND88PBzKR6//SH7Xrl2hsaOdS1S977tRbW1tdR1/sl9/j3aOuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKE37e8Y3velNoYGWL18eyu/YsSOUnzZtWig/d+7cmrPNzc2hsZ/61KeG8pVKpa75rq6uUL6joyOUHxwcDOUbGxtD+d27d9clm1L8vtfT0xPKV6vVUD46/76+vlA+Ov/oY2d0dDSUb2trqzkbvd9v3749lO/v7w/lI5edckTvB93d3TVnt2zZEhp7zpw5ofy3vvWtUL63tzeUj1q6dGkof9NNN4XymzdvDuXPOuusmrPr1q0LjR1d+6ZPnx7KR9eu6NrZ3t4eyv/sZz8L5c8777xQ/rjjjgvlx8fHa862traGxh4aGgrlYbKr9/Nf5PGfUkoNDbHjEqPzj+67NDXtdz110EW3W6L77S0tLaF81NjYWCgfve/U+/JTNkdcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFCUpv094w9+8IPQQCeffHIov2zZslC+sbExlB8bG6s5Oz4+Hho7OvdovlqthvIjIyOh/I4dO0L50dHRUD56+VtbW2vORu/3d999dyh/zTXXhPI33nhjKP/FL34xlP/0pz8dyh9zzDGh/NDQUCjf3Nwcykeet6KPm2h+586dofy6detCeR4dZsyYUXP2Zz/7WWjs973vfaH84sWLQ/mbb745lI9uuxx//PGh/FVXXRXK9/f3h/K/+tWvas52d3eHxu7s7AzlZ86cGcpH73sbNmwI5R/zmMeE8n19faH8xz72sVD+9NNPD+W3b99eczayzQnUX3Ttrbempv2ulx5SS0tLKF+pVGrO1rsziN720fGj+YaG2DGxW7duDeV5dHPENQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFadrfM65cuTI00AUXXBDKL1myJJR/wQteEMo/4QlPqDl7/PHHh8aePn16KN/c3BzKd3V1hfIjIyOh/NjYWF3zHR0dofyXvvSlmrNPecpTQmNv2bIllJ/s5syZE8rPnj07lG9sbAzlo4+9SqVSczb6uLnppptC+cc+9rGh/NFHHx3KU4bx8fG65deuXRsaO/r43bZtWyi/cePGUL6lpSWU/8UvfhHKR56/UorPv6+vr+ZsdLurra0tlN+0aVMo/z//8z+hfHS7dfPmzaF8Z2dnKL9jx45Q/tRTTw3lr7766pqz0W3W6H0HJrvo2hM1NDRU1/FHR0dD+aam/a6XHlK1Wg3lI+65555QfvHixaF89L4Xve4HBwdD+ehtN3PmzFCeRzdHXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQlKZ6T2B/3X333aH8hz/84YM0k8lnxowZofzChQtD+fHx8VC+r6+vrvnNmzeH8tTPe97znlA++ti59tprQ/mhoaFQfv78+TVnt27dGho7Ove2trZQ/uabbw7lKUP0fjAwMFBztlqthsa+9dZbQ/k1a9aE8l1dXaF8d3d3KD9v3rxQfuXKlaF8Y2NjKH/88cfXnI1us86cObOu+ajofSfyuE0pvt22ePHiUH7JkiWh/KWXXlpzdsqUKaGxgfrq7+8P5aP73WNjY6F8c3NzKN/R0RHKRy5/pVIJjT06OhrKR0W3mZuaYtXgyMhIKG/94uE44hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAojTVewIceps3b65rHurlu9/9br2nUFerVq2q9xQgZMqUKaF8a2vrQZrJ4R+7v78/lF+8eHEoX61WQ/mtW7eG8gsWLAjl16xZE8ovWrSo5uz1118fGnvZsmWhfHt7eyg/NjYWyk+fPj2Ub2iIHVfzi1/8IpSPPnaij90tW7bUnO3s7AyNDdTX4OBgKB9duxsbG0P56Hbb9u3bQ/nm5uaas0cffXRo7OHh4VA+etsNDAyE8tG1N6qjo6Ou41M2R1wDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUJSmek8AAOChDA8Ph/IDAwM1Z8fHx0Njr1y5MpT/9a9/Hco/5jGPCeU7OztD+W3btoXyo6OjofyMGTNC+a985Ss1Z48//vjQ2C0tLaF8a2trKB953KSUUlNTbPeiubk5lO/r6wvlp0+fHspPmTIllK9WqzVno7c9UF+NjY2hfOT542CIrt27du0K5efPn19zdmxsLDR2dLsxuvaNjIyE8v39/aF8e3t7KN/Q4Jha9s29AwAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiNNV7AgAAD2XBggWh/Pj4eM3ZGTNmhMY+7rjjQvlXv/rVoXzksqeU0vLly0P5jRs3hvJ33nlnKD80NBTKn3nmmaF8RGtraygfvexPfOITQ/mlS5eG8ps2bQrlX/jCF4byK1euDOVnz54dyk+dOrXmbHNzc2hsoL6i2x5jY2N1ze/evTuUjz6HNTXVXm9Fn/sXL14cylcqlVC+oSF2TOrw8HAoX61WQ/nJLnL7HenX3f5wxDUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUJSmek8AAOChNDc3h/INDbW/Pr906dLQ2E9+8pND+ec973mh/O7du0P5W265JZTftWtXKH/22WeH8suWLQvlV6xYUXP23HPPDY19xRVXhPJf+9rXQvnx8fFQ/rbbbqvr+HfccUcof88994TyT3jCE0L5tra2mrPt7e2hseFIV61W6zp+pVKp6/ijo6OhfHS7rbW1NZRvaqq93po9e3Zo7Mg2Z0rx6z5y2VOKX/fRtbulpSWU59HNEdcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFKWp3hMAAHgoIyMjofy9995bc/a4444LjT0+Ph7Kf/Ob3wzlJ7tVq1aF8o973ONC+f/8z/+sOfuv//qvobGj9/stW7aE8sTMmjUrlG9sbKw529zcHBobjnQtLS2h/ODgYCg/OjoaykfXj+j4AwMDoXx7e3sov2vXrpqz0e22vr6+UD7y3J9SSg0NsWNSx8bGQvnofS+63cejmyOuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAAChKU70nAADwUFauXBnKVyqVmrNXX311aOzGxsZQfmRkJJSf7AYGBkL5m266KZSP3H4bNmwIjb1gwYJQfsuWLaF8vUUetymlVK1W6zr+P/zDP4Ty69atqzm7c+fO0NhAfc2ePTuUnzVrVig/ODgYyi9evDiU7+joCOUbGmo/LnPOnDl1Gzul+HZj1B133BHK9/b2hvJbt24N5Xl0c8Q1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVpqvcEAAAeyqZNm+o29v3331+3sUmpv78/lN+xY8dBmsmBW7x4cSg/ZcqUgzMRalKtVkP5n//85wdpJsDhNjo6Wtfx//3f/z2U37x5cyh/yy23hPJNTbF6qaWlJZRvbm6uOTs2NhYaO5qPzD2llBobG0P5nTt3hvLTp08P5aNrb71N9vmXzhHXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABSlUq1Wq/WeBAAAAAAA7OGIawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAgMNp8eKULrss//9nP5vSySfXby4AAFAoxTU8WGfnxL/GxpRaWyd+fu5zD/98Fi9Oqb09pa6ulHp7UzrllJTe+96U+vsP/1wAoF5KXZ87O1OaMSOls89O6a67Dv88AGCyK22NTymlyy9P6elPz/vh06en9IQnpPSpTx2cv733C9jAw1Jcw4P190/8e9rTUvrAByZ+/t73Js43OppStXp45vSlL6W0c2dKW7ak9L//d0o//nFKT31qSrt3P/T5R0YOz7wA4HApdX3u70/p7rtTmjIlpVe96vCMezDYVgCgFKWt8Z/8ZEqvfnVKf/AHKa1Zk9Lmzfl33/nOoR8beADFNRyISiWlf/7nlB772JQ6OvJCWqmktGLFxHk++tGUzjhj4uf770/pFa9I6aijUpo7N6U3vSmloaHaxm9sTOm001L6+tdT2rAhpc98Jv9+z9uM3/3ulObMSellL8u///KXU3rc4/KR2qefntLPfjbxt77whZSWLcuvIM+bl9Jf/3X+/datKb3gBSlNnZpzp56a0urVtc0XAA6Heq/P3d0pvfKVKd10U/75wUdSXXZZ/t3+2LgxpZe8JKWZM1NauDCld74z76inlNJJJ6X0uc898PzPfW5KF1+c/7+/P6U3vjHnZs3KRfqOHfm0VavydfKZz6R0zDEpzZ9f00UFgMPqcK/xO3em9La3pfSP/5jX9p6ePN6ppz6wuL7yypQe//h8+imnpPSDHzzwtNNOy6cddVRKb3jDxEFnL35xSvfem9J55+Ujyi+6qLbrBY4Qims4UF/8Yl6I+vrywvlwqtWUzjknl8l33ZXSr36V0o03pvT+90+c53GPy3/zQPT2pvSsZ6V01VUTv1u5MqWmprwIfv7zKf3Hf6T053+eS+2tW1P6i7/Ib2PesiWlgYGULrggpUsuyQvzzTendNZZ+e986EN5B3nt2nzeSy7J5TYAlKye6/P27blQPuWUmqf//7z85Sk1N6d0zz0pXX11Lr3//u/zaa98ZV7j99iwIaUf/jCl88/PP7/2tXnNv+mmnB8ZyUX23r797ZR++ct8OgBMBodzjb/mmpR27covIu/LnXem9Pznp/Sud+V95ne8I4+5Z21tb0/pX/4lr8k//WlK//3fKX3kI/m0r341v8C8511bB+vjR+BRSnENB+qtb82v2ra2ptTwCA+hX/4ypTvuSOmDH8xvIZ4+PS9qey+SN92Ud1IP1Lx5eSHco6cnH5XV0pLH+vjHU3rLW/JOdENDSr//+ykdd1wutFPKO8W33poX/z1HZO/5/ZYted6NjflI7mnTDnx+AHA41WN9fsUr8juUTjghpfHx3zwa+kCtXZvSf/1X3rnt7Exp0aK8tn/2sxPjXXVVPl9Keaf3aU9LacGClDZtyu/I+vjH87re0ZHS+96X0qWXpjQ2NjHGu9+dT58yJTZXADhcDucav2lT/u6KlpZ9j3HppfkI79///Xzw2ItelD/K80tfyqc/7Wn5aOzGxpSWLEnpD/8wpR/96EAuMfB/NdV7AjDpLFy4/+ddtSofhbV38VutPnAHslZr1z7w786b98BFfNWqvEC/+90TvxsZybmOjvxlEx/+cN4IOPHE/FEhZ56Zy+7BwfwK844dKb30pSn93d/lV40BoFT1WJ+/8IWUzj33wDIPZ82alNraUpo9e+J3S5bk36eU3278jGfkcd/61lyUv+lN+bRVq3J5fvTRD/ybDQ35yOw9DuR6AoASHM41fsaM/JnWw8P7Lq/XrPnNjwDbe73+xS/yO55/9av8ESGjoyktX77/lwH4fxxxDQfqwa/wdnTktxLtsX79xP8vWJA/Y3L79ol/O3bktwRF7NiRP0Nr78/xevC8FizIxfTeYw8MpPT2t+fTn/nMfPT15s35c7bOPTfv8HZ25i/D+PWv89ukfvjDlD7xidh8AeBQK2F93qOzc99jP5z58/OLxxs3Tvxu1aoHfh71no8LWbkypdtvT+mFL8y/X7AgXwfr1j3wcg0O5he393ikI9UAoDSHc41/8pPzkdpf/eq+zzN/fl6f97b3en3eefmgsLvvzu9w/tu/feCXSlqLYb95tEDUKafkHcjR0fwFEXt/9uTpp+eF8y//Mn+WdLWav+hw729GPhDj4yndcEMumufMyZ9TvS9/9Ef57VHXX5/H3bUrl91r1uQd4m9+M8+pqSl/qVTT/30Dxne+k3eEx8fz75ubJ04DgMnicK7PDzX2l76US+O7784f37E/5s3LO7p//uf5xeZ7703pb/4mpVe/euI8L3hBnuuf/3n+/87O/Ps5c/KL0G98Y35ROqV8pPU3v3lwLhMAlOJQrvFdXflArj/5k/wOp76+/DdWrMifY51Sflfyj36U0re+lefwjW+k9OMfp/Syl+XT93wcZ0dH/njOT37ygWPMnp0/fxt4RIpriPqnf8pHJvf25m8f3nvnsrExF8Fr16b0mMfkz6H+3d/NX+awxwkn5AXx4Zx3Xl5Ap05N6cIL86vAP/nJw398x9ln54/4+IM/yLmjj07pYx/LhfT4eP7/BQvynD7+8ZS+9rX8yu+dd+YvauzqSun441N60pNSev3rQ1cRABx2h2N93pf3vz8f4TVzZv4MzVe9av+zX/xiflvxokUpPeUpeV5vfevE6VOm5KOsr7jiN//uZz878b0V3d35Mzavv762ywAApTrUa/zrX5/SZz6Tvzhx7tz88SGve11Kv/d7+fRjjsll9bvfnT+S5H3vyy8UL1mST//0p1P60Ifyi8sXXTRRaO/xjnek9M//nOf/hjccjGsEHrUq1ere71cAAAAAAID6csQ1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNdTbRz+a0hlnHLy/d8YZ+W/uywkn5G9ZBgBqY+0GgMnF2g2TkuIaHskZZ6TU2ppSZ2dK06bln6+/vn7zWb8+pZe/PKU5c1Lq6kppyZKU/vRP9z9/880p/d7v7fv0H/0opd7e6CwBoH6s3QAwuVi7gYeguIb98YEPpNTfn9K6dSk9/vEpPf/5D32+kZFDP5dXvjKltraUbrstpR07Uvr+91M6+eSD87cPx/wB4HCwdgPA5GLtBh5EcQ0Hoq0tpQsvTGnt2pS2bEnpggvyzy95SUrd3Sl96lN5Efqrv0pp6dKUpk9P6Zxz8sK7x803p/TEJ+ZXbc8884Gn7Y9rr03pNa/Jr842NORxXv3qB55n48aUnvOcPMYpp6T0q19NnLZ4cUqXXZb//7OfzYvvu9+dX0l+9rNTeu5z88Lc2Zn/XX31gV5LAFAOazcATC7WbuD/UlzDgdi1K6V//deUFi3Ki2NKKX3pS3kR3b49//ed70zppz9N6Sc/yW8vOvbYlF72snze0dG8oD7zmXkB/tu/zX9vb294Q/63L095SkpvelNKn/tcSrff/tDn+fznU/r7v09p27aUTjstpT/+433/vZUrU2pqSunee1P67ndT+t73Uurpya909/en9LSn7e+1AwDlsXYDwORi7Qb+L8U17I+/+Iv8SuuSJfmtQt/+9sRpz352fpW1oSGl9vaUPvGJlD7ykZSOOiqllpaU3v/+vKDed19K11yT0ubNKb3nPfm0Jz0ppZe+9IFjfeIT+d++fPWrKZ19dv4iiBNOyIv5F7/4wPOcf35KJ52UF8ZXv/rhPxuspycv+i0tKU2ZcoBXDAAUytoNAJOLtRt4EMU17I+LL86v7G7YkNJ//mdKj3vcxGkLF078/+bNKQ0MpPT0p+cFt7c3vxWopSUvoOvWpTR3bkrNzROZRYsObC7d3XkBvuGG/Mrun/xJSq96VUq33jpxnjlzJv6/oyO/grsv8+blxR8AHk2s3QAwuVi7gQfxqIGovRef6dPzq6fXXZcX3D3/du9O6clPzovnunUP/DKGe++tfezOzpTe/Ob86u0tt8Tn/1A/A8CjjbUbACYXazcckTxS4GBqaEjpoovyonbfffl3W7akdOml+f+f+MSUpk1L6a//OqXh4bzQ7jltf73lLSmtWJHzw8P5s7oGBlI69dSDcxlmz05p586U7r//4Pw9ACiZtRsAJhdrNxwxFNdwsF18cf4MrWc8I3+78KmnpnTllfm05ub8OV1XXJEX0re/PaXXvvaB+Ysuyv/2ZWgof+nE9On5rUmf+UxK3/pW/tbig2H58vxlF8cfn99y9ZOfHJy/CwClsnYDwORi7YYjQqVarVbrPQkAAAAAANjDEdcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABSlaX/PWKlUDuU8KNjcuXND+e3bt4fyIyMjoXy9jY+P15yt9+OuWq2G8o2NjaH8tGnTQvkNGzaE8lENDbHXBiP3HWKi9/1S1Ps5hPppatrvTbyHFH3+HhoaCuVhsoo+70bWn3qOXYLJPv89rN3US3t7eyjf09MTyg8PD4fyW7duDeUj5s+fH8rv3r07lI92LmNjY6E81Gp/1m5HXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQlEq1Wq3u1xkrlUM9Fwp19dVXh/JtbW2h/Pj4eCh/9NFHh/KNjY2h/OjoaM3Z6GWP5u++++5QfmRkJJSfOnVqKP+Od7wjlP/e974XykfvO2NjY6E8tdvPpbF41u7J65xzzqlrvrOzM5T/6U9/GsrfdtttofzPf/7zUL6vr6/m7GR//li6dGko/5KXvCSUX7hwYSi/YsWKUP7Tn/50KB/V0FD7cUXR7b7JbrI/9vawdk9eU6ZMCeVf/vKXh/I9PT2h/I4dO0L56PPvL37xi1A+su93/PHHh8Y++eSTQ/kXvOAFofzZZ58dyke2e1JK6Y477gjlo73DmjVrQnnqZ3/WbkdcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFCUSrVare7XGSuVQz0XDpHm5uZQ/r/+679C+aamplB+ZGSkrvnGxsZQfnR0tObs+Ph4aOyo6GXftWtXKN/T0xPKv/e97w3lf/jDH4by0efN/Xx65hB4tFz3R/raHbn80fvA7/zO74Tyf/ZnfxbKb9iwIZTv6uoK5aPrR73vu5Fth+Hh4dDY9V7729raQvnodud9990Xykfvu9Hx3/Wud4Xy1M7aTdRv//Zvh/KnnXbaQZpJbX7yk5+E8tH73q233hrKv/71rw/lzz333Jqz0bXjsssuC+U/+MEPhvLTpk0L5ZcvXx7KR0X3+2+44YZQ/vbbbw/lqd3+rN2OuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKE31ngCH3rx580L51tbWUH5oaCiUr1arofzY2Fgo39QUe5hE5x8RveyNjY2h/OjoaF3HP/bYY0P5H/7wh6F8pVIJ5et534FHg3o+hn7nd34nlB8YGAjlR0ZGQvlNmzaF8kuXLg3l165dG8qPj4+H8t3d3TVnW1paQmNH146tW7eG8tG1O7rt0d/fH8pHLViwIJRfvHhxKL9q1aqasw0NsWOSoo8bqLfm5uZQ/hnPeEYo/+///u+hfGdnZyg/d+7cUH769Omh/Jw5c0L5D3zgA6F85Pb/+c9/Hhr7yiuvDOWj203PfOYzQ/nofu+yZctC+W9/+9uh/B/90R+F8l/96ldD+cjazSNzxDUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARWmq9wQ49GbMmBHKt7S0hPIDAwOhfLVaDeXHxsbqmq9UKjVno5c9MnZK8cs+ODgYyo+Pj4fyc+fODeWjorcfMHktWrQolB8aGgrlOzs7Q/m77rorlO/p6Qnljz322FB+5cqVoXzk+uvr6wuN3dQU2zyPbrfNnDkzlL/uuutC+eh9p7W1NZRvbGwM5Z/whCeE8qtWrao5a7uDI92yZctC+VtvvTWUf85znhPKRx7/KaU0PDwcykf3/aLr11Oe8pRQ/tJLL605G91v/F//63+F8r/61a9C+fXr14fyo6Ojofzq1atD+cc//vGhfPSxGx0/+tjl4TniGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoSlO9J8ChN3v27FC+oWFyv75RqVQmbX58fDw0dlS1Wg3lo/edkZGRUH7u3LmhfFT0+gPqp7e3N5SPPv9F156BgYFQft68eaH8j3/841D+zDPPDOVPPPHEUH7NmjU1Z6O3/a5du0L5o48+OpSP3nbNzc2h/OjoaF3z0bX75JNPDuW/8pWv1Jy13cGR7rd+67dC+dbW1lB+27ZtofxJJ50Uyl999dWh/A033BDKd3V1hfILFy4M5QcHB2vObt26NTT26tWrQ/nofaepKVbtzZkzJ5SPbDelFF+7t2zZEspPmzYtlI9s+9W785kMJncjCQAAAADAo47iGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoSlO9J8Cht3DhwlC+Wq3WNV+pVEL5kZGRUL6lpSWUj1z+8fHx0NgNDbHXpsbGxkL56G03Ojoays+YMSOUB45c0eeP6Nqxe/fuUH54eDiUb2qKbSKecMIJofxPfvKTUP5FL3pRKL9kyZKas42NjaGxe3p6QvkrrrgilN+4cWMoP3v27FA+uu0ybdq0UH7nzp2hfPTyA7WLPn+sW7culN+6dWsov2XLllC+u7s7lD/11FND+e3bt4fyt9xySygf2XZpbW0Njb1p06ZQftGiRaH8zTffHMp3dnaG8rNmzQrlly9fHsrff//9oXx022HBggU1Z1evXh0a+0jgiGsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAitJU7wlw6C1cuDCUHx8fr2u+tbU1lI+qVCqhfLVarTk7NjYWGruxsTGUj8w9pfh1F73vTJs2LZQHjlynnXZaKN/c3BzKR58/lyxZEspv2LAhlG9paQnlH/vYx4byP/7xj0P58847r+bs3LlzQ2NH575r165Q/uSTTw7lBwYGQvkZM2aE8rt37w7lBwcHQ/l58+aF8kDturq6Qvnh4eFQPrrfHd33uvbaa0P5np6eUP62224L5bdt2xbK9/f315w988wzQ2NH15777rsvlD/qqKNC+ejaHbnuU0pp2bJloXx022np0qWhfPS5h4fniGsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAitJU7wlw6M2fPz+UHx8fP0gzqU1Dw+R+fSVy/VWr1dDY9b7uKpVKKB+977W1tYXywJHrMY95TCg/ODgYykefvxsbG0P5sbGxUD76/D86OhrKz5o1K5S//PLLa85GL3v0up8xY0YoH117Ozs7Q/nu7u5QfmhoKJSPbntFH/vHHntszdnbb789NDaUYNq0aTVnm5pi9UZ/f38oP3v27FB+zZo1ofxLX/rSUP7KK68M5X/rt34rlG9ubg7lv/Od79Scjd53ottd06dPD+V37NgRyvf09ITy0cfObbfdFspH1s6UUurt7Q3lBwYGQnke3uRuBAEAAAAAeNRRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARWmq9wQ49Hp7e0P58fHxUH5kZCSUb2lpCeWjope/UqnUJZtSfO7RfEND7LWxsbGxUH7KlCmhPHDkWrRoUSg/MDAQyjc3N4fy8+fPD+XvvPPOUD4q+vzd2dkZyjc2Ntacja7d0bVv165doXxTU2z3YOfOnaH80qVLQ/nW1tZQfsuWLaF8V1dXKH/qqafWnL399ttDY8Nkt379+lC+r68vlJ8+fXoov3HjxlD+V7/6VSgf7Q3+4z/+I5Rvb28P5bdt21Zzdvv27aGxo9tdd9xxRygf7Vx27NgRyke3u6L3vejlj277RLf9eHiOuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKE31ngCH3rRp00L50dHRUL6pKXY3Gx4eDuUbGmKvz0Qvf2T8xsbG0NjVajWUj6pUKqF8dP5r1qwJ5WfMmBHKb968OZQH6ufoo48O5W+77bZQfsqUKaF8c3NzKD916tRQfsOGDaF8dP3o6+sL5SOXPzr37u7uUP7ee+8N5Xft2hXK79ixI5Tv7OwM5Xfu3BnKR7c7o7f/8ccfH8rDZBdZ/6KPn+jzX/T5a/HixaH8qlWrQvl58+aF8meccUYoH10/urq6as5GO4trr702lG9tbQ3l29raQvloZ7RixYpQ/rTTTgvlv/KVr4TyIyMjoXy0M+LhOeIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKI01XsCHHqzZs0K5devXx/KNzc3h/JjY2N1HX98fDyUj6hUKnXNRzU0xF4bq1aroXxra2soP3/+/FB+8+bNoTwQE3n+b2qKbSKNjo6G8u3t7aF8W1tbKD8yMhLKR5//o/Pv6ekJ5QcHB2vORrcbdu/eHcpPmTIllI/e99esWRPKz549O5SPXn/Rx150u3XevHmhPEx2kcdQ9PlveHg4lI/ue0ybNi2UX7VqVSi/YsWKUH7JkiWhfF9fXyg/derUmrObNm0KjR197l6wYEEoH117t23bFso3NjaG8tHOqaurK5SPrt2dnZ01Z6OPuyOBI64BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKEpTvSfAodfV1RXKr127NpSvVqt1zTc1xe7mQ0NDoXx0/vXU0BB7bavet31jY2Mo39vbG8oD9bVgwYKas9HHf6VSCeWjz18dHR2hfHT+LS0toXx07d6yZUsoPz4+XnO23ut+9LaL3nd6enpC+ajodu+UKVNC+eHh4VB+0aJFoTxMdsuXL685u3r16tDY8+fPD+VnzZoVyl966aWh/ODgYCgfue5TSmnTpk2h/NSpU0P5yO1/0kknhca+6qqrQvnFixeH8ieeeGIo/+Mf/ziUP+6440L5O++8M5SPbrffddddoXx7e3soz8NzxDUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARWmq9wR4ZD09PaF8c3NzKN/QUN/XN8bGxkL56PwbGxtD+Wq1WnO2qSn2EK33dRcVnX/0vj99+vRQHqivWbNm1ZwdHx8PjT06OhrKd3R0hPJTp04N5aPP/9u2bQvlo2vvnDlzQvnNmzfXnI2u3b29vaF8X19fKB+97+7evTuUv+eee0L5efPmhfLRtX/r1q2hfPT+A5Pd0UcfXXN206ZNobHb29tD+ejatXjx4lD+Zz/7WSgf3XeKXv8zZ84M5U866aSas9u3bw+N/ZjHPCaUX7t2bSh/7bXXhvJPfvKTQ/lrrrkmlJ89e3Yo39/fH8pHH/uR/YbI/kpKKd1///2h/GTgiGsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoChN9Z4Aj2zZsmWhfFtbWyjf3Nwcyjc0TO7XR6KXv7Oz8yDN5MD19/eH8o2NjaF8pVIJ5Xfv3h3Kt7S0hPILFy4M5YH6Ouqoo2rOjo+Ph8bu6ekJ5WfMmBHKb9++PZS/++67Q/nFixeH8tVqNZSPbvtE7jurVq0KjV3v7baRkZFQvru7O5Rfu3ZtKP/4xz8+lN+wYUMov2XLllA+su3U3t4eGju63QUHw80331xz9hnPeEZo7Ojzz/z580P5qLGxsVC+tbU1lD/99NND+Z///OehfGT9nDJlSmjsuXPnhvJ33XVXKB+970XXruh259SpU0P56PW/YsWKUD6y7RXtbI4Ek7tRBAAAAADgUUdxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAUpaneE+CRLVu2LJQfHh4O5cfHx0P5hobY6yPR+Tc3N4fyTU2xh0lvb2/N2Wq1Ghq7v78/lI9e942NjaH82NhYXfNHH310KA/UV+T5d2RkJDR2pVIJ5adNmxbK33///aF8dO2LGhoaCuW3b98eyu/atavmbHTti4ydUkqtra11zXd3d4fyGzZsCOWj2traQvm1a9eG8sccc0zN2eXLl4fGXrFiRSgPB8O5555bc3bnzp2hsefPnx/KX3/99aH8L3/5y1D+qKOOCuUf97jHhfLXXXddKL9x48ZQ/vTTT685e9ddd4XGvu+++0L5wcHBUP6Vr3xlKP+FL3whlI8+9qJr/3HHHRfKr169OpSfMWNGzdnFixeHxr7llltC+cnAEdcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFKWp3hPgkXV2dobyDQ2x1yfGx8dD+eHh4VB+ZGQklG9qit3NBwcHQ/ldu3bVnN2xY0do7Oh119jYGMpHb/vodR8df9asWaE8UF/d3d01Z7du3Roae2BgIJRvaWkJ5a+++upQvrW1NZQfGxsL5dva2kL5mTNnhvJ33XVXKB8R3e6L3O9TSunOO+8M5bu6ukL56HW/c+fOUL65uTmUnzp1aigfee6I3negBO3t7TVnb7311tDYo6Ojofypp54aym/bti2Unz9/fig/Y8aMUD669p588smhfH9/f83Zjo6O0Nj3339/KB+9723ZsiWUP/roo0P57du3h/LRzqmnpyeUj3Zmxx9/fM3Z6HbPLbfcEspPBo64BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoTfWeAI9s4cKFofzQ0FAov2vXrlB+ypQpoXxUY2NjKD8wMBDKb968ueZsU1PsITo+Ph7Kt7a2hvJjY2OhfEtLSyhfqVRC+Tlz5oTyQH1FHsM7d+4MjR19/m1rawvlh4eHQ/kZM2aE8hs3bgzlZ86cGcqvX78+lG9ubq4529HRERp79+7dofzIyEgoH91u6uvrC+Wj992GhthxOdFtrx07doTykW2vrq6u0NhQgl//+tc1Z9vb20Njv+hFLwrl165dG8qffPLJoXx0v/W///u/Q/no83f0Oey2226rObt06dLQ2G9729tC+Z///Oeh/MqVK0P5+++/P5SPuuGGG0L5Cy64IJRfsGBBKH/rrbfWnI0+bx0JHHENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFEU1wAAAAAAFEVxDQAAAABAURTXAAAAAAAURXENAAAAAEBRFNcAAAAAABRFcQ0AAAAAQFGa6j0BHtmJJ54Yym/YsCGUnzJlSijf2NgYyre1tYXyUS0tLaF8tVqtOdvUFHuIRq/7yNxTSqmhob6vjY2NjYXys2fPPkgzAeph2rRpNWc7OjpCY8+fPz+U//Wvfx3K9/f3h/JdXV2hfG9vbyg/ffr0UD667XDPPffUnB0aGgqN3draGsrPnDkzlO/r6wvlo7d99LZbuXJlKD9r1qxQPrrtEdnuXLx4cWhsOBiam5tD+WXLltWc/dznPhcaO7LdcDDccMMNofyrXvWqUP6WW24J5detWxfKL1q0KJQ//fTTa85+4xvfCI0dXTuit330+T/yuEsppZtuuimUj2633nXXXaF8dNtjYGCg5uxtt90WGvtI4IhrAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIqiuAYAAAAAoCiKawAAAAAAiqK4BgAAAACgKIprAAAAAACKorgGAAAAAKAoimsAAAAAAIrSVO8J8Mhmz54dyo+Pj4fy1Wo1lB8bGwvlm5rqezdtaIi9vlOpVGrORm+7aD562VtbW0P5kZGRUH50dDSUb2lpCeWB+hocHKw5G33+2b17dyi/Y8eOUL65uTmUj67d/f39oXx7e3sov2rVqlB+165dNWenTJkSGntgYCCUr/dtH91uHB4eDuW3b98eyp9wwgmhfPS+29fXV3P2qKOOCo0NB0N3d3cov27dupqzT3rSk0JjL168OJS/8cYbQ/knP/nJofytt94aym/bti2Uf+ITnxjK/+hHPwrllyxZUnP2Oc95Tmjs73//+6H8scceG8pv3LgxlN+6dWsoP2vWrFD+17/+dSgf2W5LKb7tMX369Jqz0esuus07GTjiGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiKK4BAAAAACiK4hoAAAAAgKIorgEAAAAAKIriGgAAAACAoiiuAQAAAAAoiuIaAAAAAICiNNV7AjyywcHBUL61tTWU7+zsDOWj8x8bGwvlx8fHQ/mGhtjrO42NjaF8RFNT7CFerVZD+ehl7+7uDuVHR0dD+XredkBcW1tbzdnm5uaDOJMDNzIyEsr39vaG8tHnz61bt4byLS0tofzAwEAoH1n/enp6QmNH597R0RHKz5gxI5SPbrdF197oYyeaj953I/e99vb20NhQgsj+S3Ttiu43b9u2LZSfM2dOKL9q1apQftmyZaH88uXLQ/no9bdu3bqas1OnTg2N/axnPSuUj659N910Uygf3W4bHh4O5aPbrdu3bw/l582bF8pffvnlNWdPOeWU0NhHAkdcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFAUxTUAAAAAAEVRXAMAAAAAUBTFNQAAAAAARVFcAwAAAABQFMU1AAAAAABFUVwDAAAAAFCUpnpPgEfW3t4eyg8NDYXylUollG9oiL0+Uq1WQ/nx8fFQPnr5I/noZY9e92NjY6F89LqPjh9V7/t+9PqDI13kMRh9/NX7+Xv79u2hfHNzcyh/1FFHhfIDAwOh/NSpU0P5pqbaN5F3794dGjuqq6srlI/edzZu3BjKR2+74eHhUH7Xrl2hfGtraygfee5oa2sLjQ0HQ/Qx3NfXV3M2unZE95tvueWWuuYXLlwYyq9atSqU//73vx/K11P0+XP16tWh/ObNm0P56HZrS0tLKL9u3bq65k8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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "📈 PROJECT SUMMARY\n",
      "============================================================\n",
      "🏆 Final Test Accuracy: 0.9239 (92.39%)\n",
      "⏱️ Training Time: 462.8 seconds\n",
      "📊 Model Parameters: 1,703,050\n",
      "🚀 Deployment Status: ✅ GOOD - Suitable for most applications\n",
      "============================================================\n",
      "🎉 Project completed successfully!\n"
     ]
    }
   ],
   "source": [
    "# 项目主控制器\n",
    "class SmartWardrobeProject:\n",
    "    \"\"\"智能衣柜项目主控制器\"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.config = SystemConfig()\n",
    "        self.data_manager = DataManager(self.config)\n",
    "        self.data_analyzer = DataAnalyzer(self.data_manager)\n",
    "        self.model_builder = ModelBuilder(self.config, self.data_manager)\n",
    "        self.model_evaluator = ModelEvaluator(self.data_manager)\n",
    "        self.error_analyzer = ErrorAnalyzer(self.data_manager)\n",
    "        \n",
    "        # 系统状态\n",
    "        self.model = None\n",
    "        self.test_accuracy = 0.0\n",
    "    \n",
    "    def run_complete_pipeline(self):\n",
    "        \"\"\"运行完整项目流程\"\"\"\n",
    "        print(\"🚀 Starting Smart Wardrobe Project Pipeline\")\n",
    "        \n",
    "        # 1. 数据加载和预处理\n",
    "        print(\"*\" * 30 , '1. 数据加载和预处理', \"*\" * 30)\n",
    "        (x_train, y_train), (x_test, y_test) = self.data_manager.load_data()\n",
    "                \n",
    "        # 2. 数据分析\n",
    "        print(\"*\" * 30 , '2. 数据分析', \"*\" * 30)\n",
    "        self.data_analyzer.comprehensive_analysis(x_train, y_train)\n",
    "                \n",
    "        # 3. 模型构建和训练\n",
    "        print(\"*\" * 30 , '3. 模型构建和训练', \"*\" * 30)\n",
    "        self.model = self.model_builder.build_model()\n",
    "        history, training_time = self.model_builder.train_model(\n",
    "            self.model, x_train, y_train, x_test, y_test\n",
    "        )\n",
    "                \n",
    "        # 4. 模型评估\n",
    "        print(\"*\" * 30 , '4. 模型评估', \"*\" * 30)\n",
    "        self.test_accuracy, y_pred = self.model_evaluator.comprehensive_evaluation(\n",
    "            self.model, x_test, y_test, history\n",
    "        )\n",
    "                \n",
    "        # 5. 系统演示\n",
    "        print(\"*\" * 30 , '5. 系统演示', \"*\" * 30)\n",
    "        wardrobe_system = SmartWardrobeSystem(self.model, self.data_manager)\n",
    "        wardrobe_system.demonstrate(x_test, y_test)\n",
    "                \n",
    "        # 6. 错误分析\n",
    "        print(\"*\" * 30 , '6. 错误分析', \"*\" * 30)\n",
    "        self.error_analyzer.analyze_errors(y_test, y_pred, x_test)\n",
    "        \n",
    "        # 7. 项目总结\n",
    "        self._generate_project_summary(training_time)\n",
    "        print(\"🎉 Project completed successfully!\")\n",
    "    \n",
    "    def _generate_project_summary(self, training_time):\n",
    "        \"\"\"生成项目总结\"\"\"\n",
    "        print(\"\\n\" + \"=\" * 60)\n",
    "        print(\"📈 PROJECT SUMMARY\")\n",
    "        print(\"=\" * 60)\n",
    "        \n",
    "        print(f\"🏆 Final Test Accuracy: {self.test_accuracy:.4f} ({self.test_accuracy*100:.2f}%)\")\n",
    "        print(f\"⏱️ Training Time: {training_time:.1f} seconds\")\n",
    "        print(f\"📊 Model Parameters: {self.model.count_params():,}\")\n",
    "        \n",
    "        # 部署建议\n",
    "        if self.test_accuracy > 0.95:\n",
    "            status = \"🎉 EXCELLENT - Ready for production\"\n",
    "        elif self.test_accuracy > 0.90:\n",
    "            status = \"✅ GOOD - Suitable for most applications\"\n",
    "        else:\n",
    "            status = \"⚠️  NEEDS IMPROVEMENT\"\n",
    "        \n",
    "        print(f\"🚀 Deployment Status: {status}\")\n",
    "        print(\"=\" * 60)\n",
    "\n",
    "# 运行项目\n",
    "if __name__ == \"__main__\":\n",
    "    project = SmartWardrobeProject()\n",
    "    project.run_complete_pipeline()"
   ]
  }
 ],
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